{"as_of":"2026-08-21T00:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:04167ea071923d26b60f048c163769ac00aa5934a9ae49ed653a0142cf7e1e81","coverage":[{"denominator":41,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":41,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T18:54:08.912874Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-30T12:41:24.208394Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-30T12:44:39.295174Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"cited_work":{"arxiv_id":"2507.06937","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.06937","snapshot_observed_at":"2026-06-30T12:44:39.295174Z","title":"arXiv preprint arXiv:2507.06937 (2025)","venue":null,"work_id":"eebbb02a-54b9-4313-9390-0b512c273ddf","year":2025},"citing_paper":{"arxiv_id":"2605.24787","last_updated":"2026-05-24T00:17:01Z","snapshot_observed_at":"2026-08-02T02:02:08.671372Z","submitted_at":"2026-05-24T00:17:01Z","title":"SurgRFO: Foundation Model Based Compositional Synthesis of Critical Retained Foreign Objects in Intraoperative Chest X-rays","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-30T00:18:08.349398Z"},"links":{"cited_paper":"/paper/2507.06937","citing_paper":"/paper/2605.24787"},"observation_digest":"sha256:56192004dcf5be00b080f5c86de0ba63799885c807b92e5ba8a1e2adc9e39666","observation_id":"6fcf91e6-7a03-4101-a43a-efa7d7e932cf","resolution":{"observed_at":"2026-06-30T00:24:04.251749Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"cited_work":{"arxiv_id":"2507.06937","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.06937","snapshot_observed_at":"2026-06-30T12:44:39.295174Z","title":"arXiv preprint arXiv:2507.06937 (2025)","venue":null,"work_id":"eebbb02a-54b9-4313-9390-0b512c273ddf","year":2025},"citing_paper":{"arxiv_id":"2605.24789","last_updated":"2026-05-24T00:24:50Z","snapshot_observed_at":"2026-08-12T13:19:34.634117Z","submitted_at":"2026-05-24T00:24:50Z","title":"Self-Supervised Contrastive Learning for Cardiac MR Sequence Classification","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-30T12:41:24.208394Z"},"links":{"cited_paper":"/paper/2507.06937","citing_paper":"/paper/2605.24789"},"observation_digest":"sha256:0e8367cc2d00864edf78098d6df2d721aa2e9a85897b8b133d8ecd122d9d96c7","observation_id":"d050dc91-10b8-4a8e-8a53-274aaf4948f6","resolution":{"observed_at":"2026-06-30T12:44:39.297033Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2507.06937/citation-record","integrity":"/paper/2507.06937/integrity","json":"/paper/2507.06937/citation-record.json","paper":"/paper/2507.06937"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:17.249028Z","title":"Ct-based data generation for foreign object detection on a single x-ray projection","venue":null,"work_id":"67db9f03-37ae-47f7-89d0-cec6bc55af14","year":2023},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:03.824112Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:6d5ffe66f070736c450e260924bbc69e2b125f4faeb9bb53d7ec04da0ceaa9a3","observation_id":"c139fab7-5a06-4f46-bfd2-b3bddc1f76d5","resolution":{"observed_at":"2026-08-06T18:54:17.375287Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:17.011492Z","title":"Modified map-seeking circuit: Use of computer-aided detection in locating postoperative retained foreign bodies","venue":null,"work_id":"078011f2-e7be-406a-a403-3e452217ca8f","year":2012},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:03.896261Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:c54115f218f94b085a7fc5aaba7569f69175984b8b3e34b15c879e80eef5d829","observation_id":"54a1681f-915c-472b-8940-7cbcf5bce20f","resolution":{"observed_at":"2026-08-06T18:54:17.092780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:04.014094Z","title":"Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:04.014094Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:8ed183d963c146825090c265227c44127014bc69f821bc3639416d5f181fa5ff","observation_id":"c2682a70-1b87-4c03-8321-ff5760682f2d","resolution":{"observed_at":"2026-08-06T18:54:04.014094Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:04.124899Z","title":"A vision–language foundation model for the generation of realistic chest x-ray images","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:04.124899Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:d6adc98a3896227a617e31935d12e07b6864bd414973b9087ba22a5e8b2cebc0","observation_id":"ac963f85-d006-405a-ba68-4e5263e6dc7a","resolution":{"observed_at":"2026-08-06T18:54:04.124899Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:16.749159Z","title":"Free response approach to measurement and characterization of radiographic observer performance","venue":null,"work_id":"613ba6c4-4048-4bbe-9f1a-bbb5c6390aa3","year":1978},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:04.265157Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:619181384b09e23df9c2857278c64471d90f78a1e240e4432244b33edfc6a2eb","observation_id":"4fe3d20f-39fd-4059-a1d5-eb3b41e07839","resolution":{"observed_at":"2026-08-06T18:54:16.827109Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:16.520763Z","title":"Incidence and characteristics of potential and actual retained foreign object events in surgical patients","venue":null,"work_id":"3fc25fe1-63a3-43b8-8a42-84369e2f38ae","year":2008},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:04.421859Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:b11d2de9b02fa0a9dc0c1cb329d0fa197fadf9409af89cf591cb368ae4a667d8","observation_id":"cc1adf28-f0d7-4d24-b9c5-278164aeee36","resolution":{"observed_at":"2026-08-06T18:54:16.618160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:16.239936Z","title":"Operating characteristics, signal detectability, and the method of free response","venue":null,"work_id":"e4ca53aa-941b-4e14-a578-d2d7e3c49bbe","year":1961},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:04.581795Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:8af5ed4c01998100601dd5a17b165c4e72743419200cd4bdb85439066370437f","observation_id":"4b626bc7-8186-4abe-9173-b467cf3e6a19","resolution":{"observed_at":"2026-08-06T18:54:16.382331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:16.023563Z","title":"Preventable errors in the operating room: retained foreign bodies after surgery—part i","venue":null,"work_id":"97182edb-2ef9-41e2-a459-b8268073eebe","year":2007},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:04.705886Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:30bc062ad6b251686331daa2ef323be98314ffb291eec4ff6e2fae48dad0de2c","observation_id":"a56e9c57-7ca0-41a5-81f3-a31f1cf38cef","resolution":{"observed_at":"2026-08-06T18:54:16.129175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:04.826496Z","title":"Fast r-cnn","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:04.826496Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:68b6ce0930efa6949f979ca2fe2cdc07c52542140df8e99d18a64309072bd1ad","observation_id":"fcfea931-92d6-46e3-8387-e2e8fcfcb02a","resolution":{"observed_at":"2026-08-06T18:54:04.826496Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:15.723661Z","title":"Foreign object detection and removal to improve automated analysis of chest radiographs","venue":null,"work_id":"2ee4cfc2-97bf-4481-bf90-4b9e7eec6090","year":2013},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:04.943434Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:73a27cb5afc0048df1f32a3812f46faf76dbe3adae7c908171103fdaf82eef25","observation_id":"5df0aefa-f8f5-4959-984d-ae17881e8995","resolution":{"observed_at":"2026-08-06T18:54:15.833606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:15.430071Z","title":"Is synthetic data generation effective in maintaining clinical biomarkers? investigating diffusion models across diverse imaging modalities","venue":null,"work_id":"4ec87eb7-8827-4067-b083-1af2e2a2bbb8","year":2025},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:05.075487Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:7673aae1bc57a3963f30cdc3e18a900f58f05c62220228f150b9eb530c91da88","observation_id":"1fc778fc-7f05-43d7-a3a4-c25df9c3412d","resolution":{"observed_at":"2026-08-06T18:54:15.592278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:15.124536Z","title":"Denoising diffusion probabilistic models for addressing data limitations in chest x-ray classification","venue":null,"work_id":"b7c89d02-862c-4332-b613-99c590723819","year":2024},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:05.183475Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:d108a9e445f7831973dfa9f5f3379bcf7abaf933ee0bf282e134de7c86d2be7f","observation_id":"acd2071a-d052-4783-be0e-3eb5ab7ad58e","resolution":{"observed_at":"2026-08-06T18:54:15.266230Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:05.296429Z","title":"Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:05.296429Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:ad12f5c9aa35a54c42a480294634991f90ca4bb45d6303365ae8d582f7fc0df9","observation_id":"7fb9db50-fa41-42af-97a9-aa8319214c9a","resolution":{"observed_at":"2026-08-06T18:54:05.296429Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:14.841484Z","title":"Object-cxr: Automatic detection of foreign objects on chest x-rays, 2020","venue":null,"work_id":"c8405ef1-1e55-4a39-a872-7e420daa17f8","year":2020},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:05.415396Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:7de2933395aeef2244c7cb9ff84d589c612cac183fcbad806391c20a12d48b15","observation_id":"5b3ed58b-472d-41db-b4fa-813904e241cf","resolution":{"observed_at":"2026-08-06T18:54:14.968261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:14.583743Z","title":"Yolov5 by ultralytics, 2020","venue":null,"work_id":"1a610bc2-2834-4e9b-bd5f-6be65a0e5115","year":2020},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:05.529499Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:f6b715be293f421f4c50e6faeec89d94d450ee0a5e7a5f4b5bc5357ce774170e","observation_id":"5ff639c3-ade5-46f6-9e92-06d3c3f556a4","resolution":{"observed_at":"2026-08-06T18:54:14.721120Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:14.309753Z","title":"Center for clinical data analysis (ccda), 2024","venue":null,"work_id":"a62589b6-60e7-4e22-8f47-a73bcd6c849d","year":2024},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:05.639957Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:cb2b5b51065ca0ac051622f043f31227e6fa4e0673f1bcc223296efb528c7c55","observation_id":"1287463a-199b-4461-aa87-d69bbf8bf0c1","resolution":{"observed_at":"2026-08-06T18:54:14.448502Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:14.035451Z","title":"A deep learning model based on fusion images of chest radiography and x-ray sponge images supports human visual character- istics of retained surgical items detection","venue":null,"work_id":"de0ce223-cb12-473e-b066-e10d3dd7f352","year":2023},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:05.805384Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:c68ab33c9ca9dfa1b96ff8b92403d75912dffcfe7c808d04c93ad80879784230","observation_id":"364fe56c-c4d5-4596-9289-255b1ae3227b","resolution":{"observed_at":"2026-08-06T18:54:14.175436Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:13.763471Z","title":"Synthetically enhanced: unveiling synthetic data’s potential in medical imaging research","venue":null,"work_id":"01b901df-eab2-4f81-b909-ddec33483f95","year":2024},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:05.970723Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:e5544cfbd8880051dd8696ac3d59a307aba625b60a66a800238f9bd254353ebe","observation_id":"0a91bd85-c7f0-4015-b8cb-27789fcecec9","resolution":{"observed_at":"2026-08-06T18:54:13.893709Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:13.548431Z","title":"Chest x-ray foreign objects detection using artificial intelligence","venue":null,"work_id":"4b097b62-fdb6-4888-aa33-81cc0ee313df","year":2023},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:06.086291Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:1f2226ab42e06e96b7dec5e1637fc8a54e208e013d308d8565be22315b13c247","observation_id":"31e4635e-8dc5-4fae-a918-4e1b702b6512","resolution":{"observed_at":"2026-08-06T18:54:13.644327Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:13.357308Z","title":"Imaging of retained surgical items: A pictorial review including new innovations","venue":null,"work_id":"790a41f9-f49a-41c3-a292-8f3884d11664","year":2017},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:06.242883Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:1385c8f029d2713dc3dc2646b1d50ab2455b7266e6db22394f20f8c776c9b07a","observation_id":"5574dcb0-7d13-4551-818a-2a940f3d1205","resolution":{"observed_at":"2026-08-06T18:54:13.448989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:06.377143Z","title":"Focal loss for dense object detection","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:06.377143Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:ce8dbd0eb956431aab5880e9450e7c56d03e4688b5133251218b272d0bca5382","observation_id":"213f0635-ec5f-478e-b443-5cd12168c885","resolution":{"observed_at":"2026-08-06T18:54:06.377143Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:13.102845Z","title":"Retained foreign bodies after surgery","venue":null,"work_id":"391c9a99-8073-4a6d-8766-b0d2db375cd4","year":2007},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:06.520671Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:7f31a7a7c3d6551d2141b4769d6de5bd66cfaf17f884320a10fc5f68b6652c58","observation_id":"21aefff1-b652-4f67-9843-9d8025493807","resolution":{"observed_at":"2026-08-06T18:54:13.237628Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.01539","last_updated":"2024-01-03T04:35:58Z","snapshot_observed_at":"2026-08-16T14:30:11.774620Z","submitted_at":"2024-01-03T04:35:58Z","title":"DDPM based X-ray Image Synthesizer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.01539","snapshot_observed_at":"2026-08-06T18:54:06.656155Z","title":"Ddpm based x-ray image synthesizer","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:06.656155Z"},"links":{"cited_paper":"/paper/2401.01539","citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:9ecd772201d699894b4258aedef93974aee3b4900406a4282a6d347703d7523d","observation_id":"4a7576b1-6877-42ec-bcdc-35133403730f","resolution":{"observed_at":"2026-08-06T18:54:06.656155Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:12.883412Z","title":"Central venous catheter guidewire retention: lessons from england’s never event database","venue":null,"work_id":"a61efcaf-5d25-49b8-8ffb-fcf72e4d8aa4","year":2022},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:06.754819Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:857cd2441ccb5841d1059c442ff6aafba7c5c71d20a1762f02e7a99ad5adb299","observation_id":"17bd993a-e5df-42bb-b665-219ded771d95","resolution":{"observed_at":"2026-08-06T18:54:12.988746Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:12.717601Z","title":"Imaging of retained surgical sponges in the abdomen and pelvis","venue":null,"work_id":"d4d7664a-ae44-4dc0-b876-4365971639bf","year":2003},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:06.882684Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:aaf908992d45b2e774544e13f1916488b957c77e7b6f2c40b5d3c527cea4dd6c","observation_id":"6873c63f-7c27-46f2-90bf-002a98ba260b","resolution":{"observed_at":"2026-08-06T18:54:12.802252Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:12.456903Z","title":"Generic foreign object detection in chest x-rays","venue":null,"work_id":"e92cec6f-8dc0-4fe7-a426-a3af6369d5fc","year":2021},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:07.032012Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:38458471a9b8f6c4f75db7eb070e0c7ea69de228ea46fca3beb686f455f998ec","observation_id":"68a3a33c-8a85-47e9-b739-e0a5bd43a6a9","resolution":{"observed_at":"2026-08-06T18:54:12.569818Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:12.170866Z","title":"Characteristics of retained foreign bodies and near-miss events in the operating room: a ten-year experience at one institution","venue":null,"work_id":"3bc540a7-314d-4874-a2d1-efb094154bd3","year":2023},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:07.167738Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:01e576a9c58ae83b16a4ea6076b966869a62da1d6f5739463a22f0eab8c6df81","observation_id":"0c0cb499-8d90-4fea-b031-1cf861914fcb","resolution":{"observed_at":"2026-08-06T18:54:12.288042Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:11.934083Z","title":"Fcos: A simple and strong anchor-free object detector","venue":null,"work_id":"20dabd57-d392-48dc-9464-7b2d54b03fbb","year":1922},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:07.306977Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:a1255cbb7114f65b268bf9cd42ffc11a991b281db4eab41054fc7b32f555f854","observation_id":"b4212147-d86d-4dd2-8856-59d16ea523ac","resolution":{"observed_at":"2026-08-06T18:54:12.026903Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02151","last_updated":"2024-03-04T16:00:56Z","snapshot_observed_at":"2026-07-06T17:39:16.540553Z","submitted_at":"2024-03-04T16:00:56Z","title":"TripoSR: Fast 3D Object Reconstruction from a Single Image","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.02151","snapshot_observed_at":"2026-08-06T18:54:07.440665Z","title":"Triposr: Fast 3d object reconstruction from a single image","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:07.440665Z"},"links":{"cited_paper":"/paper/2403.02151","citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:23434a2e4162522ce4730175bd02426fd79a13ed306affdf9064be64299fd4c7","observation_id":"ec376d16-8aeb-4975-afef-af537992e43f","resolution":{"observed_at":"2026-08-06T18:54:07.440665Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:11.702911Z","title":"Artificial intelligence in radiology: where are we going? EBioMedicine, 109, 2024","venue":null,"work_id":"d47bf705-b7d2-4d36-afb1-da93b9417086","year":2024},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:07.580932Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:181052a6592d260af988ccb4dea7fa499f116ea5988bc994bb4a81909e7f52c3","observation_id":"98f989dc-b267-4bc7-903a-b47bd7f6fcd1","resolution":{"observed_at":"2026-08-06T18:54:11.823412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:11.462052Z","title":"Enabling machine learning in x-ray-based procedures via realistic simulation of image formation","venue":null,"work_id":"23ece8b4-d690-4e09-a3d5-61f896476ef6","year":2019},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:07.712288Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:b0b2ff5a00d9e7a8df0be2fb88137012db3a56ab355127a2b020341d9ae2ab3b","observation_id":"0e0ea1b3-0f8b-4494-b36e-31ccb056353a","resolution":{"observed_at":"2026-08-06T18:54:11.574072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:11.173267Z","title":"Deepdrr–a catalyst for machine learning in fluoroscopy-guided procedures","venue":null,"work_id":"dd797627-f48b-412a-b9a6-ba167a51ad91","year":2018},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:07.801927Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:c2046f9b7b34c5e4c73b0632a2c68648ed54c0ff949dc8eb76be156fdeb04499","observation_id":"1efb392c-4d0e-443b-84f5-620dc9dadd43","resolution":{"observed_at":"2026-08-06T18:54:11.332389Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:10.882569Z","title":"Retained guidewires after intraoperative placement of central venous catheters","venue":null,"work_id":"e950a472-95cd-4786-83c3-79ae417f687f","year":2013},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:07.928420Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:9191776ce04bd825889af71a9dce7690a7e82266f5777ec73348d5b11f326344","observation_id":"b57110d2-9ada-40eb-9371-8af41e510303","resolution":{"observed_at":"2026-08-06T18:54:11.023509Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:10.588733Z","title":"Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases","venue":null,"work_id":"4737b133-100c-405a-b13c-13ca190db797","year":2017},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:08.043644Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:3846aa0faada26d6da819e84d68a145800d94d9d39b46bcc6f79e472d8a4feaa","observation_id":"d8ef0226-0b3a-4117-9f05-d1feac8ea562","resolution":{"observed_at":"2026-08-06T18:54:10.734018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:10.338933Z","title":"Enhancing vision-language models for medical imaging: bridging the 3d gap with innovative slice selection","venue":null,"work_id":"eb05ab0b-bc7f-49d1-998d-faa3fea876da","year":2024},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:08.165536Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:b92550d53fad2edb6a667328d3edbef23ddbf31fa1c5f43aa7aca91ffb0bcae8","observation_id":"99fc6cda-10cd-49f8-837b-cb646a36f741","resolution":{"observed_at":"2026-08-06T18:54:10.468003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:10.132474Z","title":"Car- dcros: A dataset and benchmark for enhancing cardiovascular artery segmentation through disconnected components repair and open curve snake","venue":null,"work_id":"84b4d853-be24-4d49-be9a-75ea4d8c99cb","year":2024},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:08.285121Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:4757849dbe6fc39a8a1633ea7d84e0be267287c81c39bb07b5a049180e6aabd6","observation_id":"7dec2cc9-51b8-4340-862a-d951f373b75a","resolution":{"observed_at":"2026-08-06T18:54:10.217114Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:08.420373Z","title":"Totalsegmentator: robust segmentation of 104 anatomic structures in ct images","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:08.420373Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:89c637571c64512e18ce65f45fa5c938d094ff337d53f22885153a0043b1edab","observation_id":"2ac2c063-a97f-4d8c-bd1c-b57b116394ed","resolution":{"observed_at":"2026-08-06T18:54:08.420373Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:09.854481Z","title":"Management of foreign bodies of the upper gastrointestinal tract","venue":null,"work_id":"c32ba2eb-f372-4a4e-8bd3-e78c480816ec","year":1988},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:08.537406Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:adf6040286168bcd6900062d7b561eb47184c406c7b2ba5065c532b8b09ec4c1","observation_id":"08b26fcf-b6ca-4ae8-b670-e94de51d29cf","resolution":{"observed_at":"2026-08-06T18:54:09.999201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:09.584704Z","title":"Retained surgical items at chest imaging","venue":null,"work_id":"15ea7a65-265a-41ac-a384-0ebe7add3dc3","year":2021},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:08.636165Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:6d6fb57ab82f361ff17f5a0bb1b8fe216224bc93538298aa6e0ff301dde43b56","observation_id":"53bc3c8a-772e-4c63-8961-5f4068f59438","resolution":{"observed_at":"2026-08-06T18:54:09.733504Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:09.405310Z","title":"Retained surgical foreign bodies after surgery","venue":null,"work_id":"65d692bd-6722-4780-9e9d-8f643a64dc52","year":2017},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:08.771518Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:20c3a6c2a05aa838363ef259f646cd2a3c100ee74fb0a40fc08bc37150a7e5a3","observation_id":"30ad65a2-1562-42c3-ba83-5dd0f16a1aeb","resolution":{"observed_at":"2026-08-06T18:54:09.496972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:54:09.136265Z","title":"Utility of Artificial Intelligence to Detect Retained Foreign Objects on Radiograph","venue":null,"work_id":"9aee4431-95b8-4239-830b-a431707082d5","year":2020},"citing_paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:08.912874Z"},"links":{"citing_paper":"/paper/2507.06937"},"observation_digest":"sha256:386151b649b7a778b3664ab9ec22c2718003e6a41ed201537e00747a82a7c46b","observation_id":"4e2069c6-6563-4de2-8af0-8d13d580f23d","resolution":{"observed_at":"2026-08-06T18:54:09.274747Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.06937","last_updated":"2025-07-09T15:18:06Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-11T15:32:28.956544Z","submitted_at":"2025-07-09T15:18:06Z","title":"Dataset and Benchmark for Enhancing Critical Retained Foreign Object Detection"},"reference_resolution":{"displayed":41,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":33},"total_outbound_references":41},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 2 inbound Pith citation observations for arXiv:2507.06937."}