{"as_of":"2026-08-13T17:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8d80d3b81c416f7affcf0f6b06d4f222d992488dda87cdb46b453798fd89c010","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T17:52:01.611240Z","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":"2026-07-04T07:59:40.269543Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2405.12833","last_updated":"2025-03-06T17:18:49Z","snapshot_observed_at":"2026-08-13T01:28:44.099984Z","submitted_at":"2024-05-21T14:37:35Z","title":"A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.12833","snapshot_observed_at":"2026-08-12T17:52:01.611240Z","title":"arXiv preprint arXiv:2405.12833 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.12195","last_updated":"2024-11-19T03:27:05Z","snapshot_observed_at":"2026-08-12T17:47:02.815503Z","submitted_at":"2024-11-19T03:27:05Z","title":"A Survey of Medical Vision-and-Language Applications and Their Techniques","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T17:52:01.611240Z"},"links":{"cited_paper":"/paper/2405.12833","citing_paper":"/paper/2411.12195"},"observation_digest":"sha256:07fd68e41f8dcbdc45b65eeeec7b435cd293e409db557855cc5ccd4812689225","observation_id":"94795a31-19ea-4dbb-9c9d-cb4b78f103ef","resolution":{"observed_at":"2026-08-12T17:52:01.611240Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.12833","last_updated":"2025-03-06T17:18:49Z","snapshot_observed_at":"2026-08-13T01:28:44.099984Z","submitted_at":"2024-05-21T14:37:35Z","title":"A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.12833","snapshot_observed_at":"2026-08-12T15:39:32.770344Z","title":"A survey of deep learning-based radiology report generation using multimodal data,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.14039","last_updated":"2024-11-21T11:41:42Z","snapshot_observed_at":"2026-08-12T15:33:41.093355Z","submitted_at":"2024-11-21T11:41:42Z","title":"Uterine Ultrasound Image Captioning Using Deep Learning Techniques","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T15:39:32.770344Z"},"links":{"cited_paper":"/paper/2405.12833","citing_paper":"/paper/2411.14039"},"observation_digest":"sha256:b804b3484f37c2edcf05e380c4da34d1855a6791d597e92fbe3b24894e3cac67","observation_id":"e8abf85a-be1b-43dc-bda4-6f3ef12262ac","resolution":{"observed_at":"2026-08-12T15:39:32.770344Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.12833","last_updated":"2025-03-06T17:18:49Z","snapshot_observed_at":"2026-08-13T01:28:44.099984Z","submitted_at":"2024-05-21T14:37:35Z","title":"A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.12833","snapshot_observed_at":"2026-08-03T20:12:54.142406Z","title":"A survey of deep learning-based radiology report generation using multimodal data.arXiv preprint arXiv:2405.12833, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.20956","last_updated":"2026-07-20T10:46:07Z","snapshot_observed_at":"2026-08-12T12:08:38.435085Z","submitted_at":"2025-11-26T01:22:29Z","title":"BUSTR: Descriptor-Aware Vision-Language Learning for Breast Ultrasound Report Generation","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T20:12:54.142406Z"},"links":{"cited_paper":"/paper/2405.12833","citing_paper":"/paper/2511.20956"},"observation_digest":"sha256:d2e01a5397285f1ab88c44aaf97b0cd9c97f00b088624556e1f26597069231c4","observation_id":"50e4c603-bc22-4515-a8e1-30e63fa11e02","resolution":{"observed_at":"2026-08-03T20:12:54.142406Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.12833","last_updated":"2025-03-06T17:18:49Z","snapshot_observed_at":"2026-08-13T01:28:44.099984Z","submitted_at":"2024-05-21T14:37:35Z","title":"A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Data","version":2},"cited_work":{"arxiv_id":"2405.12833","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.12833","snapshot_observed_at":"2026-07-04T07:59:40.269543Z","title":"A survey of deep learning-based radiology report generation using multimodal data","venue":null,"work_id":"3ab6b19d-9bcb-4907-9b80-60ca2b2b196f","year":2024},"citing_paper":{"arxiv_id":"2604.22989","last_updated":"2026-04-24T20:03:04Z","snapshot_observed_at":"2026-07-06T23:09:19.041332Z","submitted_at":"2026-04-24T20:03:04Z","title":"CheXmix: Unified Generative Pretraining for Vision Language Models in Medical Imaging","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-08T12:22:11.995334Z"},"links":{"cited_paper":"/paper/2405.12833","citing_paper":"/paper/2604.22989"},"observation_digest":"sha256:a05f08f87a946ad1e2c2503711102f230b1cb45f270a1286e946115262ac987f","observation_id":"487d5d92-26db-4cde-bb8f-859f01af9a91","resolution":{"observed_at":"2026-05-11T19:16:09.799011Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.12833","last_updated":"2025-03-06T17:18:49Z","snapshot_observed_at":"2026-08-13T01:28:44.099984Z","submitted_at":"2024-05-21T14:37:35Z","title":"A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Data","version":2},"cited_work":{"arxiv_id":"2405.12833","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.12833","snapshot_observed_at":"2026-07-04T07:59:40.269543Z","title":"A survey of deep learning-based radiology report generation using multimodal data","venue":null,"work_id":"3ab6b19d-9bcb-4907-9b80-60ca2b2b196f","year":2024},"citing_paper":{"arxiv_id":"2606.21915","last_updated":"2026-06-20T07:18:13Z","snapshot_observed_at":"2026-08-13T04:51:02.371027Z","submitted_at":"2026-06-20T07:18:13Z","title":"GTA-Net: Cooperative Game Theory for Vision-Language Alignment in Chest X-Ray Report Generation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-26T12:20:50.289678Z"},"links":{"cited_paper":"/paper/2405.12833","citing_paper":"/paper/2606.21915"},"observation_digest":"sha256:684754c4655cb0a844c94faa0f0501489eb4f59290fe041b63e4454165fa1741","observation_id":"49f30445-ad5d-4d9b-b6d5-36d69fa86235","resolution":{"observed_at":"2026-07-04T07:59:40.270769Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2405.12833/citation-record","integrity":"/paper/2405.12833/integrity","json":"/paper/2405.12833/citation-record.json","paper":"/paper/2405.12833"},"outbound":[],"paper":{"arxiv_id":"2405.12833","last_updated":"2025-03-06T17:18:49Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-13T01:28:44.099984Z","submitted_at":"2024-05-21T14:37:35Z","title":"A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Data"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2405.12833."}