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Paper Citation Record · LEDGER

Evaluating large language models in medical applications: a survey

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.07468.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2405.07468 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T11:48:43.975259Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

4
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 8b17b27c-6c21-4344-880b-0904fea0f7a0 · inbound

AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions cites this paper.

AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions Evaluating large language models in medical applications: a survey

Reference 126

Resolution
verified exact
arxiv_id, observed 2026-05-23T21:55:50.072951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-23T21:54:26.670284Z digest=sha256:cea7574b0776dc43065284c013ca6e14a04ec18763cf973559e18e182f869c6e

Observation e27da6c9-b8ec-41e6-9714-eae61d865583 · inbound

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents cites this paper.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Evaluating large language models in medical applications: a survey

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-05T11:48:43.975259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T11:48:43.975259Z digest=sha256:cc8001e7dc88cb3edd5f259814cf1f16e6340fcf78ce74c87bcad8232e8d7150