Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2309.07430.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T03:04:47.683597Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-24T04:13:53.248419Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 3c794b3f-87ae-40a2-8c88-55cb4766eafe · inbound
Data-Centric Foundation Models in Computational Healthcare: A Survey cites this paper.
Data-Centric Foundation Models in Computational Healthcare: A Survey Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization
Reference 299
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T04:13:53.251850Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 4ee3d69c-04c6-4986-84ea-77c1bcea927a · inbound
Benchmarking Knowledge-Extraction Attack and Defense on Retrieval-Augmented Generation cites this paper.
Benchmarking Knowledge-Extraction Attack and Defense on Retrieval-Augmented Generation Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T03:04:47.683597Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6b814f44-824e-4dee-8702-9ad6d9d8895f · inbound
Beyond Literal Summarization: Redefining Hallucination for Medical SOAP Note Evaluation cites this paper.
Beyond Literal Summarization: Redefining Hallucination for Medical SOAP Note Evaluation Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T10:44:38.359994Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.