{"as_of":"2026-08-21T18:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:38d57507c5656ed0f65871f50adfb5eec784fe95a59a14dd1d09fe1f912321d9","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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":"2026-08-07T14:54:49.443992Z","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":"2026-07-02T08:26:48.117227Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2311.01301","last_updated":"2025-08-16T16:33:01Z","snapshot_observed_at":"2026-08-20T20:09:21.071965Z","submitted_at":"2023-11-02T15:15:47Z","title":"TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.01301","snapshot_observed_at":"2026-08-07T14:54:49.443992Z","title":"Trialscope: a unifying causal framework for scaling real-world evidence generation with biomedical language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.16982","last_updated":"2025-05-22T17:52:59Z","snapshot_observed_at":"2026-08-15T15:40:29.328686Z","submitted_at":"2025-05-22T17:52:59Z","title":"Beyond Correlation: Towards Causal Large Language Model Agents in Biomedicine","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T14:54:49.443992Z"},"links":{"cited_paper":"/paper/2311.01301","citing_paper":"/paper/2505.16982"},"observation_digest":"sha256:31f4d4f6673648f45b940dd88e45fd4603645780f0369130bcc8de84f3654375","observation_id":"a6ed6836-8434-42d7-9d4f-2eb33c4e0da9","resolution":{"observed_at":"2026-08-07T14:54:49.443992Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.01301","last_updated":"2025-08-16T16:33:01Z","snapshot_observed_at":"2026-08-20T20:09:21.071965Z","submitted_at":"2023-11-02T15:15:47Z","title":"TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models","version":3},"cited_work":{"arxiv_id":"2311.01301","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.01301","snapshot_observed_at":"2026-07-02T08:26:48.117227Z","title":"arXiv preprint arXiv:2311.01301 , year=","venue":null,"work_id":"083009c3-8f11-4fbc-93e6-c7cf393b3388","year":null},"citing_paper":{"arxiv_id":"2606.05436","last_updated":"2026-06-03T20:58:43Z","snapshot_observed_at":"2026-08-13T19:03:23.436726Z","submitted_at":"2026-06-03T20:58:43Z","title":"Ten Headache Specialists versus Artificial Intelligence for Clinical Literature Summarization: A Critical Evaluation and Comparison","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-06-28T06:03:59.798126Z"},"links":{"cited_paper":"/paper/2311.01301","citing_paper":"/paper/2606.05436"},"observation_digest":"sha256:b877a3c7e3a9daceabdfc8167865f071c6725eaaffc60b861d918cdca34d4192","observation_id":"c9318127-2341-4721-ad03-7dce26ddcee1","resolution":{"observed_at":"2026-07-02T08:26:48.118533Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2311.01301/citation-record","integrity":"/paper/2311.01301/integrity","json":"/paper/2311.01301/citation-record.json","paper":"/paper/2311.01301"},"outbound":[],"paper":{"arxiv_id":"2311.01301","last_updated":"2025-08-16T16:33:01Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-20T20:09:21.071965Z","submitted_at":"2023-11-02T15:15:47Z","title":"TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models"},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2311.01301."}