{"as_of":"2026-08-18T03:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bc07b89ba1fc7676bbc30092c8e12fc90de1f12e0c45d9a182b48dbb679dae2e","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T08:27:23.981232Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.21117/citation-record","integrity":"/paper/2607.21117/integrity","json":"/paper/2607.21117/citation-record.json","paper":"/paper/2607.21117"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T08:27:21.512992Z","title":"Diagnosis and classification of diabetes mellitus,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:21.512992Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:934cade5e4953f2145a963bbf356dbf71e79a7e0bedd6ef68911efd06774034c","observation_id":"f07bc3d2-b843-477e-99b9-8aad8d0f929b","resolution":{"observed_at":"2026-08-01T08:27:21.512992Z","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":"2026-08-01T08:27:21.620528Z","title":"Perioperative hyperglycemia and risk of adverse events among patients with and without diabetes,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:21.620528Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:0bfa37af5b8ebc927791394c1aad0b6c1729aee38c78c5e8e865897876d57425","observation_id":"bb57ea0d-8389-4e85-b0ec-7219103520c1","resolution":{"observed_at":"2026-08-01T08:27:21.620528Z","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":"2026-08-01T08:27:21.727741Z","title":"Hypoglycemia in diabetes,","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:21.727741Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:321b6c0c866abb40906b0479c087041d0d5f203ecb845f70749bd862dfa8f50a","observation_id":"c3ab37b2-ed12-408a-85a7-3cb7d69b32ce","resolution":{"observed_at":"2026-08-01T08:27:21.727741Z","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":"2026-08-01T08:27:21.842756Z","title":"Arima models,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:21.842756Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:cfb2295c5fa4a8c6e19476a2087961930ca087248507a6279c259442de35fbbe","observation_id":"ad2de21c-2423-4073-8053-1aaf62822a2f","resolution":{"observed_at":"2026-08-01T08:27:21.842756Z","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":"2026-08-01T08:27:21.962664Z","title":"Blood glucose level prediction as time-series modeling using sequence-to-sequence neural networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:21.962664Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:cbd988966cc02591e307664ac31a372f70086dc11ce9a29970f71a538867bc47","observation_id":"a72fee2d-f1e1-4a56-a917-4fbd40eaea36","resolution":{"observed_at":"2026-08-01T08:27:21.962664Z","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":"2026-08-01T08:27:22.066450Z","title":"A deep learning approach for blood glucose prediction of type 1 diabetes,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:22.066450Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:ca84e0ad0637d80d2636050476377d7d0fe8463cf208d99d2b7f47cf64e5c7c3","observation_id":"9548f27b-515d-4d3b-9bfd-c9a654c1c21b","resolution":{"observed_at":"2026-08-01T08:27:22.066450Z","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":"2026-08-01T08:27:22.175777Z","title":"Blood glucose prediction with variance estimation using recurrent neural networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:22.175777Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:4db16032043d678cb68e9e3e38972a1e13b1c1d63baaf3b6ac30fd76db0af556","observation_id":"55edea8d-080f-43e1-8412-0067b297af16","resolution":{"observed_at":"2026-08-01T08:27:22.175777Z","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":"2026-08-01T08:27:22.225621Z","title":"Convolutional recurrent neural networks for glucose prediction,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:22.225621Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:5048bb262a911551ca319abfaa81c2d1b4ad6ffa774f83c6570c63aaa111303a","observation_id":"a50215fb-b7e3-4511-a5fb-e8d021790345","resolution":{"observed_at":"2026-08-01T08:27:22.225621Z","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":"2026-08-01T08:27:22.304689Z","title":"Bgformer: An improved informer model to enhance blood glucose prediction,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:22.304689Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:17ccbc36ef9a799aff4795261aaba0f260218ee1418563a02bbbb83ec86098d7","observation_id":"0a205485-da0d-4771-8e81-d74269280493","resolution":{"observed_at":"2026-08-01T08:27:22.304689Z","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":"2026-08-01T08:27:22.363504Z","title":"A hybrid transformer-lstm model apply to glucose prediction,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:22.363504Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:27b8680806662e5a5136750f3d714586f9ee482a8de6ceefcd92886a1814ec0f","observation_id":"6099d2b3-7476-48b7-b5db-c45fb62da72d","resolution":{"observed_at":"2026-08-01T08:27:22.363504Z","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":"2026-08-01T08:27:22.429708Z","title":"Deep multitask learning by stacked long short-term memory for predicting personalized blood glucose concentration,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:22.429708Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:39b9803880ffb6e58fdce3837b4ae4bb98ff660814698e33312ae9a50b5b6c72","observation_id":"06aafca8-618b-437c-a72d-5d395ed233c1","resolution":{"observed_at":"2026-08-01T08:27:22.429708Z","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":"2026-08-01T08:27:22.474291Z","title":"Improving detection of type-1 diabetes adverse events using gru networks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:22.474291Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:bfd7b5a49a81bfe4957114cce0ccdc232400abb916f50eed99f87efd8e014bce","observation_id":"0822b3e9-f64c-4147-8d4e-caed827482ca","resolution":{"observed_at":"2026-08-01T08:27:22.474291Z","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":"2026-08-01T08:27:22.560390Z","title":"Predicting adverse events for patients with type-1 diabetes via self-supervised learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:22.560390Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:c6bfe1010a1f2bdf7aaecd702ba6c856587163a5fd2b1b10eefd57217af3af71","observation_id":"2b20d533-a646-4b75-ab73-a3215251045f","resolution":{"observed_at":"2026-08-01T08:27:22.560390Z","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":"2026-08-01T08:27:22.615088Z","title":"Prediction of blood glucose levels in patients with type 1 diabetes via lstm neural networks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:22.615088Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:a4fabce486a9b863e41b66e03b77d2382ef4bcd5ef42dcee34148de13dbb8efc","observation_id":"b20610d2-2182-4c57-946f-c4cf77069955","resolution":{"observed_at":"2026-08-01T08:27:22.615088Z","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":"2026-08-01T08:27:22.678973Z","title":"A deep learning approach to diabetic blood glucose prediction,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:22.678973Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:adb796c2dbeb1b9cadbdd3949793824f32dcf30b1dcd56e38ed92ec5806f99ab","observation_id":"a08d35e5-3ee9-444a-b6d0-8d3664220c73","resolution":{"observed_at":"2026-08-01T08:27:22.678973Z","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":"2026-08-01T08:27:22.753926Z","title":"Adversarial multi- source transfer learning in healthcare: Application to glucose prediction for diabetic people,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:22.753926Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:11f14d27cc27d859f82b301b5992c1cb8ad4d5c9efa187f49e4337533324c84e","observation_id":"15e0360e-e506-43a8-99c6-a17895088f3c","resolution":{"observed_at":"2026-08-01T08:27:22.753926Z","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":"2026-08-01T08:27:22.805093Z","title":"The OhioT1DM Dataset for Blood Glucose Level Prediction: Update 2020,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:22.805093Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:708af9d8f38e843dcbb69fd031620be1b91fbe605562d71ad4de8dd856ab3ff7","observation_id":"18d54d9b-e060-4478-bc84-49f330694f79","resolution":{"observed_at":"2026-08-01T08:27:22.805093Z","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":"2026-08-01T08:27:22.885469Z","title":"Diatrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:22.885469Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:08864ba0ae3c8124fb040bb30d3e056a3986fb4d7b431b488ad44a062c5297dc","observation_id":"1155baec-138f-42a1-a2ca-8443be8debc2","resolution":{"observed_at":"2026-08-01T08:27:22.885469Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08915","last_updated":"2024-06-13T08:23:05Z","snapshot_observed_at":"2026-08-16T13:43:26.376499Z","submitted_at":"2024-06-13T08:23:05Z","title":"GluPredKit: Development and User Evaluation of a Standardization Software for Blood Glucose Prediction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.08915","snapshot_observed_at":"2026-08-01T08:27:22.946007Z","title":"Glupredkit: Development and user evaluation of a standardization software for blood glucose prediction,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:22.946007Z"},"links":{"cited_paper":"/paper/2406.08915","citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:c6f175f424c83cb259a71ddf0de7954dfb87415dab590fd30d01fd56e677707d","observation_id":"ce125069-4390-43e1-9bb3-74d9cbe51652","resolution":{"observed_at":"2026-08-01T08:27:22.946007Z","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":"2026-08-01T08:27:22.997483Z","title":"Interpreting blood glucose data with r package iglu,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:22.997483Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:f4929f0b4b38de2405ae68638abfe9c008622c67ce647da83b0946849d4e0f51","observation_id":"a19b10e5-6652-43e4-9f6a-653a85a52475","resolution":{"observed_at":"2026-08-01T08:27:22.997483Z","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":"2026-08-01T08:27:23.062348Z","title":"Glucostats: an efficient python library for glucose time series feature extraction and visual analysis,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:23.062348Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:100c8ce84da32712fa6286f57e0252ab7ab4f6350007732797517c6993ce55ab","observation_id":"1fd8397f-00be-48eb-818e-532c13d5b069","resolution":{"observed_at":"2026-08-01T08:27:23.062348Z","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":"2026-08-01T08:27:23.101670Z","title":"Continuous glucose monitoring time series data analysis: a time series analysis pack- age for continuous glucose monitoring data,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:23.101670Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:23c54793c586ac8ceef6aeb4c8a620d36466d14a756e70f154a2128223177d9e","observation_id":"d79bb7b9-9f3e-4d46-9673-f5676665cf58","resolution":{"observed_at":"2026-08-01T08:27:23.101670Z","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":"2026-08-01T08:27:23.148970Z","title":"T1diabetesgranada: a longitudinal multi-modal dataset of type 1 diabetes mellitus,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:23.148970Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:987198e6ec34e0cd7ad973a7239200fe72f7b969ff99e742e02ba39581efa3d9","observation_id":"563fd7b2-c864-494f-a446-0669c0a45813","resolution":{"observed_at":"2026-08-01T08:27:23.148970Z","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":"2026-08-01T08:27:23.204618Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:23.204618Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:4787f7b167dabb8d9165677078ff7132cd08c4aba115862a7a5f4e7b97d5efb5","observation_id":"67b0ad57-9f56-4cd5-91c8-cb7142b3ea91","resolution":{"observed_at":"2026-08-01T08:27:23.204618Z","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":"2026-08-01T08:27:23.259713Z","title":"Feature transformation for efficient blood glucose prediction in type 1 diabetes mellitus patients,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:23.259713Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:9058eb499e183774467128e04a357ddc2b1c8f82bb7514b3843b6a38cc63f248","observation_id":"fe020602-406d-411c-a30b-16024472bacd","resolution":{"observed_at":"2026-08-01T08:27:23.259713Z","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":"2026-08-01T08:27:23.334594Z","title":"Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:23.334594Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:aeb7745c15cf2be2101dd9d5865fde01ad7d2ccdd3f2b82fdf56a1ad0fbbd80f","observation_id":"fa857ada-d79e-473b-b8f7-71f620a4744a","resolution":{"observed_at":"2026-08-01T08:27:23.334594Z","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":"2026-08-01T08:27:23.398940Z","title":"Smote: synthetic minority over-sampling technique,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:23.398940Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:549c86785d97b609cc31fbd92402839a16494cc3665c80eb0e529baea0213198","observation_id":"bfca77d6-2ea5-45d0-96d4-ff2e9fe693d9","resolution":{"observed_at":"2026-08-01T08:27:23.398940Z","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":"2026-08-01T08:27:23.471864Z","title":"A data-driven personalized approach to predict blood glucose levels in type-1 diabetes patients exercising in free-living conditions,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:23.471864Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:42844ffa9419df147eca714dddb74c0274e28f6bf9472579cd680491c41dde42","observation_id":"24c6ded7-92ad-4f6d-a197-e1aae284323c","resolution":{"observed_at":"2026-08-01T08:27:23.471864Z","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":"2026-08-01T08:27:23.554312Z","title":"Dilated recurrent neural networks for glucose forecasting in type 1 diabetes,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:23.554312Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:f7bc7a364de4bcc453926d7db8d05609ff5cc1a0ee3d34ef2502249627fe569c","observation_id":"41f805f2-20a4-4e19-a610-1bba3a7d9e1c","resolution":{"observed_at":"2026-08-01T08:27:23.554312Z","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":"2026-08-01T08:27:23.613533Z","title":"Lightweight sequential transformers for blood glucose level prediction in type-1 diabetes,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:23.613533Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:72f2f7d073944610fbb14794138dbd4fe5b09da1248f2c3ea39af4988d47d2da","observation_id":"55f6d343-3244-4e05-a6d2-362088c7ae57","resolution":{"observed_at":"2026-08-01T08:27:23.613533Z","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":"2026-08-01T08:27:23.691419Z","title":"Evaluating clinical accuracy of systems for self-monitoring of blood glucose,","venue":null,"work_id":null,"year":1987},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:23.691419Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:e180a457ed8a5f22cefe2711e333086e477ee82165606ac6a1c019bbb3b77c47","observation_id":"3c5785af-37f7-4a2d-a3a0-d053b94c7eb6","resolution":{"observed_at":"2026-08-01T08:27:23.691419Z","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":"2026-08-01T08:27:23.781176Z","title":"Technical aspects of the parkes error grid,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:23.781176Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:2dc5d84d4943d3c1acd5bcf4e1e7dda98bb0c7198a034219a1eb2dbaae4b844f","observation_id":"1ccff482-57b5-4163-ac79-dfa9ecf9b84c","resolution":{"observed_at":"2026-08-01T08:27:23.781176Z","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":"2026-08-01T08:27:23.871784Z","title":"Sus-a quick and dirty usability scale,","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:23.871784Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:0288cda9415d766802c5981501691ae9e953ba7c0d12b571ded755e246cfb7fc","observation_id":"bce63bab-92a4-4048-ae70-e955fc722ea9","resolution":{"observed_at":"2026-08-01T08:27:23.871784Z","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":"2026-08-01T08:27:23.923259Z","title":"Measuring usability with the system usability scale (sus),","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:23.923259Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:3e7541f787cdecba605e1cd67d35feeb06458c63287cb9147451e05e7d6eb1de","observation_id":"eedfe9e8-33f5-4b97-b5a8-21978a499e6e","resolution":{"observed_at":"2026-08-01T08:27:23.923259Z","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":"2026-08-01T08:27:23.981232Z","title":"Available: http://www.measuringusability.com/sus.php","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes","version":1},"reference_index":2011,"source":"pdf_text","source_observed_at":"2026-08-01T08:27:23.981232Z"},"links":{"citing_paper":"/paper/2607.21117"},"observation_digest":"sha256:8882d000e8a5198b3f7db318e7974b1fd425bd8dc155e50a882b589c0e8e65cd","observation_id":"eed968d4-e889-4792-8d25-d10cfdcc4d9c","resolution":{"observed_at":"2026-08-01T08:27:23.981232Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.21117","last_updated":"2026-07-23T09:53:39Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-01T08:27:20.503902Z","submitted_at":"2026-07-23T09:53:39Z","title":"GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":35,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":35},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2607.21117."}