Paper Citation Record · LEDGER

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs

As of 19 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2501.13687.

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

pith.paper-citation-record.v1
2501.13687 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:45:34.256340Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T12:19:18.421552Z

measured 1 of 1 external citation measurements

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

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

Reference resolution

25 of 25 outbound references displayed

verified exact0
verified fuzzy15
unresolved9
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

0
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 0eb42885-6c5b-406a-aca3-06e991ba8119 · outbound

This paper cites https://www.cms.gov/priorities/key-initiatives/ burden-reduction/interoperability/policies-and-regulations/ cms-interoperability-and-patient-access-final-rule-cms-9115-f.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs https://www.cms.gov/priorities/key-initiatives/ burden-reduction/interoperability/policies-and-regulations/ cms-interoperability-and-patient-access-final-rule-cms-9115-f

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:45:34.656020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2549340e-035c-4f4e-8e69-3335da817daf · outbound

This paper cites Accession Number: PLAW-114publ255, PLAW-114publ255 Call Number: AE 2.110:, AE 2.110/3:, AE 2.110:114-255, AE 2.110:, AE 2.110/3:, AE 2.110:114-255 Source: DGPO, DGPO.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs Accession Number: PLAW-114publ255, PLAW-114publ255 Call Number: AE 2.110:, AE 2.110/3:, AE 2.110:114-255, AE 2.110:, AE 2.110/3:, AE 2.110:114-255 Source: DGPO, DGPO

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:45:34.639082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 67a6c207-53f8-41bd-a414-ee021f482915 · outbound

This paper cites https://www.apple.com/healthcare/ health-records/.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs https://www.apple.com/healthcare/ health-records/

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:45:34.622829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a097e9b2-7af9-443f-bcc2-2f2adbbc6609 · outbound

This paper cites Almutairi, Sulaiman Al Mashrafi, and Talib Al Kalbani.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs Almutairi, Sulaiman Al Mashrafi, and Talib Al Kalbani

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:45:34.606784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cb5e0637-deea-4cc6-800c-07be150da4c0 · outbound

This paper cites Large Language Models: A Survey.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs Large Language Models: A Survey

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T15:45:34.155667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation facf8f67-46e4-4b07-b351-5b96c5a19854 · outbound

This paper cites Health information privacy.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs Health information privacy

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:45:34.589637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 43c52a00-16d7-49e3-be68-8bfd5b502a7c · outbound

This paper cites an unresolved cited work.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-10T15:45:34.573149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 20f9c8b4-1be7-4c96-a866-52dd743a4787 · outbound

This paper cites an unresolved cited work.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-10T15:45:34.556105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 98a554d3-185a-474f-9a4c-6e6d4f98ce9b · outbound

This paper cites LLaMA: Open and efficient foundation language models.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs LLaMA: Open and efficient foundation language models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:45:34.540043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e5210ea5-d2b2-43d4-826e-5cecde08bdea · outbound

This paper cites an unresolved cited work.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-10T15:45:34.523754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 56b3fd56-8740-4b8f-bcb3-caf0a8126196 · outbound

This paper cites an unresolved cited work.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T15:45:34.187926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 204050e4-7b3c-4e0e-9b11-fcc4bf2d96f1 · outbound

This paper cites Parameter-efficient transfer learning for nlp.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs Parameter-efficient transfer learning for nlp

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T15:45:34.193297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da56cbb5-46bc-4033-ad4b-be5502ea78fb · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:45:34.486104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 87626ec7-d256-4cdc-8b0f-42247a60b2a7 · outbound

This paper cites QLoRA: Efficient finetuning of quantized LLMs.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs QLoRA: Efficient finetuning of quantized LLMs

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:45:34.469519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a25afa51-f2de-4933-8dae-a019874452af · outbound

This paper cites Yerebakan, Yoshihisa Shinagawa, and Yuan Luo.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs Yerebakan, Yoshihisa Shinagawa, and Yuan Luo

Reference 16

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T15:45:34.452720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5971e233-32f0-4bb1-8dfb-5111d02792eb · outbound

This paper cites Agentic LLM workflows for generating patient-friendly medical reports.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs Agentic LLM workflows for generating patient-friendly medical reports

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:45:34.437404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e7057d94-0280-40c4-92d4-95692ac1d308 · outbound

This paper cites LLM on FHIR – demystifying health records.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs LLM on FHIR – demystifying health records

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:45:34.421156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ead83b0a-9d8b-4b4c-8243-5d2e862eb436 · outbound

This paper cites SUPaHOT: Universally scalable and private method to demystify FHIR health records.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs SUPaHOT: Universally scalable and private method to demystify FHIR health records

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:45:34.405442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 71d3e698-bb86-4c11-8b64-ecbe9316ada1 · outbound

This paper cites Meditron-7b: Scaling medical pretraining for large language models, 2023.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs Meditron-7b: Scaling medical pretraining for large language models, 2023

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:45:34.389360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fdddbd38-e3fd-4478-9e4d-7f5beb3c44f2 · outbound

This paper cites Retrieval-augmented generation for large language models: A survey, 2024.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs Retrieval-augmented generation for large language models: A survey, 2024

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T15:45:34.233118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eab18689-774b-4699-9c05-d2ae1ec2734b · outbound

This paper cites Retrieval-augmented generation for natural language processing: A survey, 2024.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs Retrieval-augmented generation for natural language processing: A survey, 2024

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T15:45:34.238363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8031b67b-255a-42e8-8a7a-991ea4740d07 · outbound

This paper cites Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:45:34.353412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d6215040-c7bc-4e18-8ee4-e9ab0acaea31 · outbound

This paper cites Meteor: An automatic metric for mt evaluation with improved correlation with human judgments.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs Meteor: An automatic metric for mt evaluation with improved correlation with human judgments

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T15:45:34.247499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6335fc35-8082-4c08-a8f8-50351c8d985e · outbound

This paper cites LLMs as narcissistic evaluators: When ego inflates evaluation scores.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs LLMs as narcissistic evaluators: When ego inflates evaluation scores

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:45:34.327954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 68378e82-e3bd-445f-93bb-1b966b7108a3 · outbound

This paper cites resource.

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs resource

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:45:34.311034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation f191d92e-8868-4655-aabe-326a0ea23a1f · inbound

Scaling Laws for Hypernetwork-Based Knowledge Injection in Large Language Models cites this paper.

Scaling Laws for Hypernetwork-Based Knowledge Injection in Large Language Models Question Answering on Patient Medical Records with Private Fine-Tuned LLMs

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-01T12:23:39.909062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.