Paper Citation Record · LEDGER

Clinical trial cohort selection using Large Language Models on n2c2 Challenges

As of 20 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2501.11114.

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

pith.paper-citation-record.v1
2501.11114 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T18:40:53.000759Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

35 of 35 outbound references displayed

verified exact1
verified fuzzy30
unresolved4
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c8a923ca-1f08-4b0f-80a4-7a694ead4294 · outbound

This paper cites Optimizing clinical research participant selection with infor- matics,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Optimizing clinical research participant selection with infor- matics,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.398214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3d707b2a-df07-4cea-ac87-fd42f976746b · outbound

This paper cites Piloting the ehr4cr feasibility platform across europe,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Piloting the ehr4cr feasibility platform across europe,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.387860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7799fd0f-0802-4c2f-850c-b05dcca8b14b · outbound

This paper cites Efficiency and effectiveness eval- uation of an automated multi-country patient count cohort system,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Efficiency and effectiveness eval- uation of an automated multi-country patient count cohort system,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.377296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7b3fe087-7da1-46f1-929e-e106c901a7b6 · outbound

This paper cites Leveraging the ehr4cr platform to support patient inclusion in academic studies: challenges and lessons learned,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Leveraging the ehr4cr platform to support patient inclusion in academic studies: challenges and lessons learned,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.366374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 39abcbd0-10b3-4fa8-9d71-de5f19de62aa · outbound

This paper cites Formal representation of eligibility criteria: a literature review,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Formal representation of eligibility criteria: a literature review,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.356075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1a55df28-017a-430e-81b2-0cb7ca4451aa · outbound

This paper cites Dynamic categorization of clinical research eligibility criteria by hierarchical clustering,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Dynamic categorization of clinical research eligibility criteria by hierarchical clustering,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.345803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dd798169-f6c1-43da-9a33-29f101dd4d3c · outbound

This paper cites Developing a data element repository to support ehr-driven phenotype algorithm authoring and execution,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Developing a data element repository to support ehr-driven phenotype algorithm authoring and execution,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.335241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7b736f77-5105-4c38-aecb-775d650cef35 · outbound

This paper cites Cross border semantic interoperability for clinical research: the ehr4cr semantic re- sources and services,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Cross border semantic interoperability for clinical research: the ehr4cr semantic re- sources and services,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.324514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1a1c9f1e-ea05-41fc-aa85-8af6eb92e7cd · outbound

This paper cites Phekb: a catalog and workflow for creating electronic phenotype algo- rithms for transportability,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Phekb: a catalog and workflow for creating electronic phenotype algo- rithms for transportability,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.313182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 17512e1a-d50b-41d1-a381-f3a5a2fe02c3 · outbound

This paper cites n2c2 nlp research data sets.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges n2c2 nlp research data sets

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.302557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 87c95789-eef8-4149-b6c7-7ef8fa72e242 · outbound

This paper cites Identifying patient smoking status from medical discharge records,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Identifying patient smoking status from medical discharge records,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.291949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 604bfa99-dd7d-4221-bbf0-67a383a70177 · outbound

This paper cites Recognizing obesity and comorbidities in sparse data,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Recognizing obesity and comorbidities in sparse data,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.282136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e57a8abb-deeb-483b-9f82-226833dff29a · outbound

This paper cites Cohort selection for clinical trials: n2c2 2018 shared task track 1,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Cohort selection for clinical trials: n2c2 2018 shared task track 1,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.271587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 81ee4610-ff6e-4af2-8966-ff41c2b9035c · outbound

This paper cites GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models

Reference 14

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Source-reported events for the cited work

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Observation 7dbac27c-3334-4206-ad12-c48d77158193 · outbound

This paper cites A survey of gpt-3 family large language models including chatgpt and gpt-4,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges A survey of gpt-3 family large language models including chatgpt and gpt-4,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.260832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c0e0b197-afcb-42bd-8119-cee34f28b2d2 · outbound

This paper cites Embracing large language models for medical applications: opportunities and challenges,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Embracing large language models for medical applications: opportunities and challenges,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.250739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 94bc3241-e61d-414e-bb0e-8a9f2cd03558 · outbound

This paper cites Transforming clinical trials: the emerging roles of large language models,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Transforming clinical trials: the emerging roles of large language models,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.240327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ebef36de-5d5a-45b6-9661-0d6ae34bd9ea · outbound

This paper cites Scaling clinical trial matching using large language models: A case study in oncology,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Scaling clinical trial matching using large language models: A case study in oncology,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.229402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 248e41ec-d892-4509-995f-ea4dd6fae299 · outbound

This paper cites Large language models for healthcare data augmentation: An example on patient-trial matching.,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Large language models for healthcare data augmentation: An example on patient-trial matching.,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.218584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0c8bdb5a-40af-4624-8fe8-60981359992b · outbound

This paper cites Distilling large language models for matching patients to clinical trials,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Distilling large language models for matching patients to clinical trials,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.206975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e12181d1-5870-41e5-8d72-64dfdbbc8dd4 · outbound

This paper cites Prompt engineering paradigms for medical applications: scoping review and recommendations for better practices.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Prompt engineering paradigms for medical applications: scoping review and recommendations for better practices

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-10T18:40:53.058158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1a60ce86-99c6-48cf-8b31-02bd8d59d1b9 · outbound

This paper cites Building community knowledge in online competitions: motivation, practices and challenges,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Building community knowledge in online competitions: motivation, practices and challenges,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.196149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 607aeaec-17d5-4084-a5d0-4120f98e25f2 · outbound

This paper cites Clinical concept extrac- tion using transformers,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Clinical concept extrac- tion using transformers,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.185653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 40077af6-299b-4088-a19b-4c9851a9f3cb · outbound

This paper cites Zero-shot clinical trial patient matching with llms,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Zero-shot clinical trial patient matching with llms,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.175005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 027ce70f-83ca-4fc3-9ca2-51220fd3fc0f · outbound

This paper cites Utilizing large language models for enhanced clinical trial matching: A study on automation in patient screening,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Utilizing large language models for enhanced clinical trial matching: A study on automation in patient screening,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.164449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7ee6d9c9-2f07-4292-b835-d1093d7cb3d4 · outbound

This paper cites Language mod- els are few-shot learners,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Language mod- els are few-shot learners,

Reference 26

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Source-reported events for the cited work

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Observation fce01572-3a2b-4439-95c4-b8494be889cc · outbound

This paper cites MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T18:40:52.970906Z

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Observation 143197ea-bea2-4612-aafb-17cb39f17a23 · outbound

This paper cites Why johnny can’t prompt: how non-ai experts try (and fail) to design llm prompts,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Why johnny can’t prompt: how non-ai experts try (and fail) to design llm prompts,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.147203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bd1b26f2-90af-4f1c-bc19-65d08629eef4 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Chain-of-thought prompting elicits reasoning in large language models,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.136123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 292810d2-f81e-48c3-85d4-21010059b607 · outbound

This paper cites Llms are not zero-shot reasoners for biomedical information extraction,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Llms are not zero-shot reasoners for biomedical information extraction,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.123995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d3a3300d-2e48-480d-9bf3-155987c01965 · outbound

This paper cites Few shot clinical entity recog- nition in three languages: Masked language models outperform llm prompting,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Few shot clinical entity recog- nition in three languages: Masked language models outperform llm prompting,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.113043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 329f5529-db34-4280-8bbf-9a9637b8f8b2 · outbound

This paper cites Optimizing instructions and demonstrations for multi-stage language model programs,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Optimizing instructions and demonstrations for multi-stage language model programs,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.101549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6710e21b-7b98-45e4-aa95-d8312b19e61b · outbound

This paper cites Fine-tuning and prompt optimiza- tion: Two great steps that work better together,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Fine-tuning and prompt optimiza- tion: Two great steps that work better together,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.090745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 43ecd3a4-61d2-4e91-99fc-510260f13459 · outbound

This paper cites Autocriteria: a generalizable clinical trial eligibility criteria extraction system powered by large lan- guage models,.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges Autocriteria: a generalizable clinical trial eligibility criteria extraction system powered by large lan- guage models,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:53.079913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3f145365-7390-4f3a-acf8-bf48da3dff01 · outbound

This paper cites ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notes.

Clinical trial cohort selection using Large Language Models on n2c2 Challenges ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notes

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T18:40:53.000759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.