{"as_of":"2026-08-11T06:32:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2c47084306779f126cdca8bd7fc58cc0d219f8805ee208f1e5a5679073fe0929","coverage":[{"denominator":5,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T13:34:44.761341Z","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":"2026-08-10T06:31:04.303077+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/2502.07836/citation-record","integrity":"/paper/2502.07836/integrity","json":"/paper/2502.07836/citation-record.json","paper":"/paper/2502.07836"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T13:34:44.797374Z","title":"Multi-omic machine learning predictor of breast cancer therapy response,","venue":null,"work_id":"468b254e-14b5-45e5-acb7-355e22251cd9","year":2022},"citing_paper":{"arxiv_id":"2502.07836","last_updated":"2025-07-03T23:24:15Z","snapshot_observed_at":"2026-08-08T13:31:04.437361Z","submitted_at":"2025-02-11T01:44:51Z","title":"Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-08T13:34:44.752609Z"},"links":{"citing_paper":"/paper/2502.07836"},"observation_digest":"sha256:33b7c334d7cd70f52c5da36089896e68c9e4d6eac488879018c3ad24bdf31b68","observation_id":"21005a15-6ba0-4794-ba3b-ffc82d13ebc1","resolution":{"observed_at":"2026-08-08T13:34:44.801506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.03677","last_updated":"2017-03-21T21:29:50Z","snapshot_observed_at":"2026-07-06T04:36:10.443201Z","submitted_at":"2015-11-11T21:01:28Z","title":"Learning to Diagnose with LSTM Recurrent Neural Networks","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.03677","snapshot_observed_at":"2026-08-08T13:34:44.754911Z","title":"Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.07836","last_updated":"2025-07-03T23:24:15Z","snapshot_observed_at":"2026-08-08T13:31:04.437361Z","submitted_at":"2025-02-11T01:44:51Z","title":"Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data","version":3},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-08T13:34:44.754911Z"},"links":{"cited_paper":"/paper/1511.03677","citing_paper":"/paper/2502.07836"},"observation_digest":"sha256:6e196aedf47952f7c13e13ebc00bd9f991bf14277380535cf5b707143f518f8e","observation_id":"619cb7ee-1b82-4b66-afe2-1c2eae6c1152","resolution":{"observed_at":"2026-08-08T13:34:44.754911Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.01271","last_updated":"2018-04-19T14:32:38Z","snapshot_observed_at":"2026-07-06T06:26:27.965096Z","submitted_at":"2018-03-04T00:20:29Z","title":"An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.01271","snapshot_observed_at":"2026-08-08T13:34:44.757733Z","title":"Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.07836","last_updated":"2025-07-03T23:24:15Z","snapshot_observed_at":"2026-08-08T13:31:04.437361Z","submitted_at":"2025-02-11T01:44:51Z","title":"Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data","version":3},"reference_index":109,"source":"pdf_text","source_observed_at":"2026-08-08T13:34:44.757733Z"},"links":{"cited_paper":"/paper/1803.01271","citing_paper":"/paper/2502.07836"},"observation_digest":"sha256:36205a8ba6727011fcb6b6768277105fd63024de2b64c82f8ee51f2e04543046","observation_id":"bfb76894-ebc2-4b9f-9317-81f718cc5d54","resolution":{"observed_at":"2026-08-08T13:34:44.757733Z","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":"2026-08-08T13:34:44.806283Z","title":"Cancer statistics, 2024,","venue":null,"work_id":"69b6beab-d357-49a3-8893-a6f20f99229c","year":2024},"citing_paper":{"arxiv_id":"2502.07836","last_updated":"2025-07-03T23:24:15Z","snapshot_observed_at":"2026-08-08T13:31:04.437361Z","submitted_at":"2025-02-11T01:44:51Z","title":"Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data","version":3},"reference_index":141,"source":"pdf_text","source_observed_at":"2026-08-08T13:34:44.749447Z"},"links":{"citing_paper":"/paper/2502.07836"},"observation_digest":"sha256:010d139d901f08401ed957d78ddd518a652130d8aca19fe8a2018672164943fc","observation_id":"81737cfc-87f6-49b3-ae63-6aede2a571aa","resolution":{"observed_at":"2026-08-08T13:34:44.808438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01785","last_updated":"2024-02-01T21:34:34Z","snapshot_observed_at":"2026-08-10T23:31:22.470904Z","submitted_at":"2024-02-01T21:34:34Z","title":"DoubleMLDeep: Estimation of Causal Effects with Multimodal Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01785","snapshot_observed_at":"2026-08-08T13:34:44.761341Z","title":"Modeling Missing Data in Clinical Time Series with RNNs,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.07836","last_updated":"2025-07-03T23:24:15Z","snapshot_observed_at":"2026-08-08T13:31:04.437361Z","submitted_at":"2025-02-11T01:44:51Z","title":"Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data","version":3},"reference_index":154,"source":"pdf_text","source_observed_at":"2026-08-08T13:34:44.761341Z"},"links":{"cited_paper":"/paper/2402.01785","citing_paper":"/paper/2502.07836"},"observation_digest":"sha256:8d9cb464110f4ce39f28c64c86a62d232c61059e1d7f44a481715a65c7876b8b","observation_id":"028ecc0b-a00e-4485-914d-c0177e01c623","resolution":{"observed_at":"2026-08-08T13:34:44.761341Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.07836","last_updated":"2025-07-03T23:24:15Z","latest_version":3,"primary_category":"q-bio.QM","snapshot_observed_at":"2026-08-08T13:31:04.437361Z","submitted_at":"2025-02-11T01:44:51Z","title":"Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data"},"reference_resolution":{"displayed":5,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":0,"verified_fuzzy":2},"total_outbound_references":5},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 5 of 5 outbound references and 0 inbound Pith citation observations for arXiv:2502.07836."}