Paper Citation Record · LEDGER

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection

As of 9 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2506.20112.

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

pith.paper-citation-record.v1
2506.20112 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:00:15.197447Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

24 of 24 outbound references displayed

verified exact2
verified fuzzy18
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6924d2fc-b9c5-4f56-b46e-4769ee22043b · outbound

This paper cites Potential of GPT-4 for detecting errors in radiology reports: Implications for reporting accuracy.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection Potential of GPT-4 for detecting errors in radiology reports: Implications for reporting accuracy

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:00:15.717108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4078bf13-4927-4bfe-8d59-d89df737337e · outbound

This paper cites Large language models as an inexpensive and effective extra set of eyes in radiology reporting.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection Large language models as an inexpensive and effective extra set of eyes in radiology reporting

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:00:15.698376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f1c9ad99-f164-4e53-8081-cbbb3426fc28 · outbound

This paper cites Large-scale validation of the feasibility of GPT-4 as a proofreading tool for head CT reports.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection Large-scale validation of the feasibility of GPT-4 as a proofreading tool for head CT reports

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:00:15.682546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 79eb0f19-95d9-4c6a-bb41-ed82b9f291d4 · outbound

This paper cites Advancing artificial intelligence to meet breast imaging needs.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection Advancing artificial intelligence to meet breast imaging needs

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:00:15.662731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1869a3d0-c0ba-4867-8066-82c31a04d652 · outbound

This paper cites Environmental sustainability and AI in radiology: A double-edged sword.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection Environmental sustainability and AI in radiology: A double-edged sword

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:00:15.645378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 83673137-8d0b-418b-8676-287c1ff984f7 · outbound

This paper cites Enhancing diagnostic capability with multi-agents conversational large language models.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection Enhancing diagnostic capability with multi-agents conversational large language models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:00:15.627460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9ae7b068-3751-4d39-889f-9527d1eaa207 · outbound

This paper cites Medical foundation large language models for comprehensive text analysis and beyond.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection Medical foundation large language models for comprehensive text analysis and beyond

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:00:15.609237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 784cce22-24d1-4f1a-8e91-06402cc7ba98 · outbound

This paper cites Large language models for error detection in radiology reports: a comparative analysis between closed-source and privacy-compliant open-source models.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection Large language models for error detection in radiology reports: a comparative analysis between closed-source and privacy-compliant open-source models

Reference 10

Resolution: verified exact; doi, observed 2026-08-06T23:00:15.283587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation eda25b75-adc3-46c4-b48e-0fafda7ed405 · outbound

This paper cites Generative Large language models trained for detecting errors in radiology reports.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection Generative Large language models trained for detecting errors in radiology reports

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:00:15.591742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cb8950fe-1f00-40e4-ad33-78ae69a4a7a5 · outbound

This paper cites MIMIC-III, a freely accessible critical care database.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection MIMIC-III, a freely accessible critical care database

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:00:15.572619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 075bd39f-3bce-41fd-9c72-7641964ad697 · outbound

This paper cites CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:00:15.555004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 44860353-60f0-44a8-a16a-953ad4501b27 · outbound

This paper cites Preparing a collection of radiology examinations for distribution and retrieval.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection Preparing a collection of radiology examinations for distribution and retrieval

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:00:15.537008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 607e5852-4939-4010-9b17-ce6804a8bebd · outbound

This paper cites [cited 2025 Jun 11].

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection [cited 2025 Jun 11]

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:00:15.517639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7c76b404-0eac-4654-809e-901485d59134 · outbound

This paper cites [cited 2025 Jun 11].

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection [cited 2025 Jun 11]

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:00:15.495376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d43803c2-7253-45a4-a341-f8c1b4d3d5e5 · outbound

This paper cites The death of cost-minimization analysis? Health Econ.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection The death of cost-minimization analysis? Health Econ

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:00:15.474184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7abd7380-47de-45fe-935d-9d513c5cc774 · outbound

This paper cites Towards Sustainable Web Agents: A Plea for Transparency and Dedicated Metrics for Energy Consumption.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection Towards Sustainable Web Agents: A Plea for Transparency and Dedicated Metrics for Energy Consumption

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:00:15.254203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8cad7eeb-5ca4-4480-80b8-11fe11d6f9f9 · outbound

This paper cites Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T23:00:15.171494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4128a86-65d1-438f-a753-fbb477dc6d4c · outbound

This paper cites The use of confidence or fiducial limits illustrated in the case of the binomial.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection The use of confidence or fiducial limits illustrated in the case of the binomial

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:00:15.450724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 41279a59-6a2c-4d0a-bc8c-1393a079246f · outbound

This paper cites Bootstrap-based improvements for inference with clustered errors.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection Bootstrap-based improvements for inference with clustered errors

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:00:15.429682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 11a11d98-8127-4696-8b5d-d8637d3a279d · outbound

This paper cites AI as a second reader can reduce radiologists’ workload and increase accuracy in screening mammography.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection AI as a second reader can reduce radiologists’ workload and increase accuracy in screening mammography

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:00:15.409177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0579788b-dd92-43eb-87d9-b4bc169fb022 · outbound

This paper cites Crucial role of understanding in human-artificial intelligence interaction for successful clinical adoption.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection Crucial role of understanding in human-artificial intelligence interaction for successful clinical adoption

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:00:15.390046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f37a9d93-3714-49eb-b1ef-ed72301ae7db · outbound

This paper cites Energy and Policy Considerations for Deep Learning in NLP.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection Energy and Policy Considerations for Deep Learning in NLP

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-06T23:00:15.165336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86492177-d751-4f56-bf30-8462c0b310a7 · outbound

This paper cites Carbon Emissions and Large Neural Network Training.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection Carbon Emissions and Large Neural Network Training

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T23:00:15.092811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 182dd31f-5b6b-4f5d-91ef-a376291f312a · outbound

This paper cites Why Do Multi-Agent LLM Systems Fail?.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection Why Do Multi-Agent LLM Systems Fail?

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T23:00:15.100311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.