{"as_of":"2026-08-11T07:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7ae7346b6ac5457341f93f994ee16ed3dede3d5989e3408cfd1b03f97ae4c6b1","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T15:42:28.051483Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2402.15422","last_updated":"2024-06-25T17:02:10Z","snapshot_observed_at":"2026-08-04T12:39:28.707348Z","submitted_at":"2024-02-23T16:32:28Z","title":"A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.15422","snapshot_observed_at":"2026-08-03T15:42:28.051483Z","title":"A data-centric approach to generate faithful and high quality patient sum- maries with large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.16189","last_updated":"2026-06-09T06:01:03Z","snapshot_observed_at":"2026-08-06T07:13:30.291001Z","submitted_at":"2025-12-18T05:23:47Z","title":"Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation","version":4},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-03T15:42:28.051483Z"},"links":{"cited_paper":"/paper/2402.15422","citing_paper":"/paper/2512.16189"},"observation_digest":"sha256:4ad0d54c2696a478c9716d0aa00cb7e519beaab3cd1abf11e14c204c0f90c372","observation_id":"429a0a88-1db1-4bc6-bca1-b46bdc301ba7","resolution":{"observed_at":"2026-08-03T15:42:28.051483Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2402.15422/citation-record","integrity":"/paper/2402.15422/integrity","json":"/paper/2402.15422/citation-record.json","paper":"/paper/2402.15422"},"outbound":[],"paper":{"arxiv_id":"2402.15422","last_updated":"2024-06-25T17:02:10Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-04T12:39:28.707348Z","submitted_at":"2024-02-23T16:32:28Z","title":"A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large 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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2402.15422."}