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Paper Citation Record · LEDGER

The challenge of uncertainty quantification of large language models in medicine

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2504.05278.

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

pith.paper-citation-record.v1
2504.05278 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:44:15.178000Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T18:57:31.628006Z

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 ddd012bd-f656-4762-ba50-88888c623c80 · inbound

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs cites this paper.

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs The challenge of uncertainty quantification of large language models in medicine

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-07T12:44:15.178000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:44:15.178000Z digest=sha256:ac71cb20ad36a35f30b7367bbe66c11e7bccddbfa57e2575cfebba83f9aa255d

Observation ea7c5c1b-9d02-4b88-a817-e64f8e5a4641 · inbound

Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol cites this paper.

Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol The challenge of uncertainty quantification of large language models in medicine

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-05T14:55:56.599407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:55:56.599407Z digest=sha256:7a118870afea3d6a3059e13e4e1d84abcd3d2c6597853a1513f2b616c2bb1bd6

Observation 33651c28-aa52-4aba-8efc-d1a9bc3022a2 · inbound

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation cites this paper.

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation The challenge of uncertainty quantification of large language models in medicine

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-04T22:45:42.550373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:45:42.550373Z digest=sha256:f69fe84d1f7343698b85188ee54e8a12d166e493a3fcd6870331bfa58127afe9

Observation 539c49ca-cd90-44d1-8f6c-4a7e0d4b0d71 · inbound

Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances cites this paper.

Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances The challenge of uncertainty quantification of large language models in medicine

Reference 145

Resolution
unresolved
no resolver link, observed 2026-08-04T00:00:09.669918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T00:00:09.669918Z digest=sha256:b1586b851cdefd5e48406c6b7d804ea98f0167370a30c02cb4fd3787ef21833e

Observation c5421787-3fb7-41d6-81d0-dda8346e42d1 · inbound

LEC: Linear Expectation Constraints for Selection-Conditioned Risk Control in Selective Prediction and Routing Systems cites this paper.

LEC: Linear Expectation Constraints for Selection-Conditioned Risk Control in Selective Prediction and Routing Systems The challenge of uncertainty quantification of large language models in medicine

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-03T19:18:18.058963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:18:18.058963Z digest=sha256:b7a8440ccb6d0c5ff03545dd54a7dffcfdd292cfaa2882588d6f400870eac0ed

Observation 67c13e4b-5cf4-4b47-a448-bbb0ea9c08d0 · inbound

MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction cites this paper.

MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction The challenge of uncertainty quantification of large language models in medicine

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-07-04T20:00:08.335934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-25T20:53:30.125527Z digest=sha256:100c6e45673c477526378e8c467f91c81ebe3479c0f175c15ea1d9fad6d9efee

Observation 173e64c4-691a-4401-8c22-a62b4dc3463e · inbound

MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction cites this paper.

MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction The challenge of uncertainty quantification of large language models in medicine

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-06-29T18:33:51.175825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-29T05:04:46.900824Z digest=sha256:24baeedf5058b27ad9d8abbe623535ed3d982ec3ae630338e96c1d4e35442cdb

Observation d9afada4-5b1c-4fa2-8f99-e842f8179909 · inbound

Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning cites this paper.

Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning The challenge of uncertainty quantification of large language models in medicine

Reference 200

Resolution
metadata mismatch
local_arxiv, observed 2026-07-10T18:57:31.629473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-07-10T18:50:22.827472Z digest=sha256:4ccafc64436d659d9d408d6b620927c97f06e873892fdf6a224baade128c75e1