Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T23:00:15.197447Z
Paper Citation Record · LEDGER
A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection
As of 17 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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.