Paper Citation Record · LEDGER

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs

As of 17 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 6 inbound Pith citation observations for arXiv:2504.14657.

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

pith.paper-citation-record.v1
2504.14657 v2

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:46:43.762839Z

measured 67 of 67 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:00:31.577528Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T04:37:04.046203Z

Reference resolution

61 of 61 outbound references displayed

verified exact1
verified fuzzy20
unresolved40
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b292cae2-0b05-4178-bbb4-bb720f48c9b9 · outbound

This paper cites Alaa and Mihaela van der Schaar.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Alaa and Mihaela van der Schaar

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:46:44.561605Z

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 721c3513-9d99-4950-a9e8-57264fea00de · outbound

This paper cites Dk-behrt: Teaching language models international classification of disease (icd) codes using known disease descriptions.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Dk-behrt: Teaching language models international classification of disease (icd) codes using known disease descriptions

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:46:44.548029Z

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 4aa8502d-435f-41de-9c2b-c87c3ca76144 · outbound

This paper cites Medical event data standard (meds): Facilitating machine learning for health.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Medical event data standard (meds): Facilitating machine learning for health

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:46:44.534515Z

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 f4ff988c-8bc6-4fa0-b1e7-3c8754823f76 · outbound

This paper cites Leveraging large language models for decision support in personalized oncology.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Leveraging large language models for decision support in personalized oncology

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:46:44.520911Z

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 e61a143b-cfb2-4b45-9742-ca710417c99d · outbound

This paper cites Language Models are Realistic Tabular Data Generators.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Language Models are Realistic Tabular Data Generators

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.518680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d89ffbdc-bdb0-4089-9adc-c29492ba02c2 · outbound

This paper cites The uk biobank resource with deep phenotyping and genomic data.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs The uk biobank resource with deep phenotyping and genomic data

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:46:44.506963Z

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 e8272733-0c42-40a6-869d-c66da2745fbc · outbound

This paper cites Why is my classifier discriminatory? Advances in neural information processing systems, 31, 2018.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Why is my classifier discriminatory? Advances in neural information processing systems, 31, 2018

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.527598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7264f17-fe5a-4453-b068-5cd01a577815 · outbound

This paper cites Algorithmic fairness in artificial intelligence for medicine and healthcare.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Algorithmic fairness in artificial intelligence for medicine and healthcare

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.532110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ff11293-fbdb-4ecb-86c7-e142d968ea9e · outbound

This paper cites Chen, M.Y.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Chen, M.Y

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.536107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 521437f8-a13c-44f8-8f44-49e4f0c7b42c · outbound

This paper cites Does Synthetic Data Make Large Language Models More Efficient?.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Does Synthetic Data Make Large Language Models More Efficient?

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.540510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 228fd57a-730a-4991-9452-d535f98bbe89 · outbound

This paper cites Prompt engineering with chatgpt: A guide for academic writers.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Prompt engineering with chatgpt: A guide for academic writers

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:46:44.473853Z

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 d7e053e2-2cba-408f-85ae-bfed473eddf1 · outbound

This paper cites Generalization—a key challenge for responsible ai in patient-facing clinical applications.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Generalization—a key challenge for responsible ai in patient-facing clinical applications

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:46:44.460022Z

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 a32bb0eb-92c1-4b86-a91f-9d982e36d001 · outbound

This paper cites Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:46:44.446654Z

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 b7ba5905-d1e1-4dd8-9cf3-1aa7a6884236 · outbound

This paper cites Synthetic data in health care: A narrative review.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Synthetic data in health care: A narrative review

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:46:44.432844Z

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 51afd89b-2d9a-45c9-854d-58de91080e3d · outbound

This paper cites Llmsyn: Generating synthetic electronic health records without patient-level data.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Llmsyn: Generating synthetic electronic health records without patient-level data

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:46:44.419194Z

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 a0e4f11f-0e2f-4e93-8b4c-c34831d92ab5 · outbound

This paper cites Tabllm: Few-shot classification of tabular data with large language models.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Tabllm: Few-shot classification of tabular data with large language models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.565800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cf3aef6-05d6-4980-90c0-8a00fda4d367 · outbound

This paper cites Yu, and Xuyun Zhang.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Yu, and Xuyun Zhang

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:46:44.396147Z

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 5ed2965c-7a14-44df-99ce-407bd7a7f1e0 · outbound

This paper cites Genhpf: General healthcare predictive framework for multi-task multi-source learning.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Genhpf: General healthcare predictive framework for multi-task multi-source learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:46:44.382832Z

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 74f4de57-c874-4c61-8057-0a137eb0fb03 · outbound

This paper cites OpenAI o1 System Card.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs OpenAI o1 System Card

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.578844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c6e7528-a77d-415a-87aa-f6ffdf606ceb · outbound

This paper cites What disease does this patient have? a large-scale open domain question answering dataset from medical exams.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs What disease does this patient have? a large-scale open domain question answering dataset from medical exams

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.583166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b40be6d8-d14f-4ea5-8258-97611a01f069 · outbound

This paper cites an unresolved cited work.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Unresolved cited work

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.587373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f2f0391-b034-4b32-8628-d75f4aef4e69 · outbound

This paper cites Generalizability of predictive models for intensive care unit patients.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Generalizability of predictive models for intensive care unit patients

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.591437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a17656b8-786b-4ab1-803f-9fbaa3891611 · outbound

This paper cites Pate-gan: Generating synthetic data with differential privacy guarantees.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Pate-gan: Generating synthetic data with differential privacy guarantees

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.595788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e245f68-16d8-4b70-b705-b585320263a0 · outbound

This paper cites Synthetic Data -- what, why and how?.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Synthetic Data -- what, why and how?

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.600005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94d7d97b-9e7b-4e15-9e7c-ab4f50977505 · outbound

This paper cites M ed E x QA : Medical question answering benchmark with multiple explanations.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs M ed E x QA : Medical question answering benchmark with multiple explanations

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.604212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a21616df-3606-4f22-8381-698757fe1813 · outbound

This paper cites Meds decentralized, extensible validation (meds-dev) benchmark: Establishing reproducibility and comparability in ml for health.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Meds decentralized, extensible validation (meds-dev) benchmark: Establishing reproducibility and comparability in ml for health

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.608340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c20be3a-8929-4559-983d-cbdac0bdd548 · outbound

This paper cites Kullback-leibler divergence, 1951.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Kullback-leibler divergence, 1951

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.612565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 782c48ed-61ef-4537-8647-f09bcebc510d · outbound

This paper cites Medsyn: Llm-based synthetic medical text generation framework.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Medsyn: Llm-based synthetic medical text generation framework

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:46:44.335760Z

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 952ddd2e-84dc-4c2d-8461-ef20aceb846b · outbound

This paper cites Can Large Language Models abstract Medical Coded Language?.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Can Large Language Models abstract Medical Coded Language?

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.620499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 488be444-fbb8-48a5-afa5-df69438023ac · outbound

This paper cites Enhancing Antibiotic Stewardship using a Natural Language Approach for Better Feature Representation.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Enhancing Antibiotic Stewardship using a Natural Language Approach for Better Feature Representation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.625214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3729d0a0-e8ff-400f-8912-e819de29607c · outbound

This paper cites Emergency Department Decision Support using Clinical Pseudo-notes.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Emergency Department Decision Support using Clinical Pseudo-notes

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.629630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8d5a59b-e41a-4e95-bc14-13d094264712 · outbound

This paper cites FEET: A Framework for Evaluating Embedding Techniques.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs FEET: A Framework for Evaluating Embedding Techniques

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:46:44.046546Z

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 aa39e1d6-b63d-421a-a5f0-094a14c23278 · outbound

This paper cites Clinical ModernBERT: An efficient and long context encoder for biomedical text.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Clinical ModernBERT: An efficient and long context encoder for biomedical text

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.638357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31885d8f-7679-4e95-9872-7955cd8d9d1a · outbound

This paper cites MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial Network for Synthesizing Tabular Data.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial Network for Synthesizing Tabular Data

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.642623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 918646ff-da1e-439f-a047-f83f28390f8f · outbound

This paper cites Goggle: Generative modelling for tabular data by learning relational structure.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Goggle: Generative modelling for tabular data by learning relational structure

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:46:44.322376Z

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 43a02675-bec9-418d-b23d-456cb4737c76 · outbound

This paper cites Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.651000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abf9e6f9-d2b6-4ad9-86af-9e37a81ec4d1 · outbound

This paper cites Synthetic data for deep learning, volume 174.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Synthetic data for deep learning, volume 174

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:46:44.308635Z

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 3259ead7-168e-4c50-aa32-232866ad0e27 · outbound

This paper cites Text Serialization and Their Relationship with the Conventional Paradigms of Tabular Machine Learning.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Text Serialization and Their Relationship with the Conventional Paradigms of Tabular Machine Learning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.659455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd8e5bf2-9c39-468a-9e8a-05cbac033b89 · outbound

This paper cites Schema Matching with Large Language Models: an Experimental Study.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Schema Matching with Large Language Models: an Experimental Study

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.663840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7130e111-f26a-4c86-aaa2-eac97c176634 · outbound

This paper cites The eicu collaborative research database, a freely available multi-center database for critical care research.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs The eicu collaborative research database, a freely available multi-center database for critical care research

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.668137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52115f6f-0d35-4ddb-899a-fa15cbe73f15 · outbound

This paper cites Synthetic data.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Synthetic data

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:46:44.286281Z

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 2eebb2db-b9ce-4319-9694-749ffd0e0549 · outbound

This paper cites Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.676862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 421ba9d5-bf76-4d4c-9113-4114ce4decc0 · outbound

This paper cites Tabular data: Deep learning is not all you need.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Tabular data: Deep learning is not all you need

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.681465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f594bcd-074b-4ae4-bd5f-18eef6ea4283 · outbound

This paper cites Towards Expert-Level Medical Question Answering with Large Language Models.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Towards Expert-Level Medical Question Answering with Large Language Models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.686462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3d0c7af-fd3a-448b-b224-6d5cf73f11dd · outbound

This paper cites REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.690815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d8a81cf-f706-4114-af8b-948f268be20d · outbound

This paper cites Large language models are poor medical coders—benchmarking of medical code querying.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Large language models are poor medical coders—benchmarking of medical code querying

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.695377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20a993ed-3550-49a2-b437-0acce59dc08d · outbound

This paper cites MOTOR: A Time-To-Event Foundation Model For Structured Medical Records.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs MOTOR: A Time-To-Event Foundation Model For Structured Medical Records

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.699846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70c6636b-f020-42a6-a316-ef982cf167d3 · outbound

This paper cites Differentially Private Tabular Data Synthesis using Large Language Models.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Differentially Private Tabular Data Synthesis using Large Language Models

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.704486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5727147a-54c2-4002-bead-738f34a1f939 · outbound

This paper cites Solving olympiad geometry without human demonstrations.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Solving olympiad geometry without human demonstrations

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.708852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 537c9d64-c968-4057-a984-2c4c08158e5a · outbound

This paper cites Decaf: Generating fair synthetic data using causally-aware generative networks, 2021.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Decaf: Generating fair synthetic data using causally-aware generative networks, 2021

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:46:44.243983Z

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 0e458ade-337c-407c-83d1-a0eb4e84cb03 · outbound

This paper cites Large language models as synthetic electronic health record data generators.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Large language models as synthetic electronic health record data generators

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:46:44.230154Z

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 74bcab68-a2c3-4201-a119-69086974ff06 · outbound

This paper cites Large language models in medical and healthcare fields: applications, advances, and challenges.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Large language models in medical and healthcare fields: applications, advances, and challenges

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:46:44.215300Z

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 e4b24e90-975d-428c-a073-edf7c78a23dc · outbound

This paper cites Improving Text Embeddings with Large Language Models.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Improving Text Embeddings with Large Language Models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.726957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e26ac9c2-f9f2-43f0-bc39-a38451ce2115 · outbound

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

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Chain-of-thought prompting elicits reasoning in large language models

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.731169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd4b0a71-eda6-4f4e-93fb-28192f1ed226 · outbound

This paper cites Generating synthetic electronic health record data using generative adversarial networks: Tutorial.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Generating synthetic electronic health record data using generative adversarial networks: Tutorial

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:46:44.193141Z

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 dbaf98c5-b76f-48eb-95bb-38477a5746e6 · outbound

This paper cites Integrating UMLS Knowledge into Large Language Models for Medical Question Answering.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Integrating UMLS Knowledge into Large Language Models for Medical Question Answering

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.739697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6437acb7-581c-40f6-a4da-34e2a9b1ea8e · outbound

This paper cites Time-series generative adversarial networks.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Time-series generative adversarial networks

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.744178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d256daa-6689-44bb-b77b-80205938df38 · outbound

This paper cites Anonymization through data synthesis using generative adversarial networks (ads-gan).

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Anonymization through data synthesis using generative adversarial networks (ads-gan)

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.748381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be7a51b5-fde0-40e6-b834-436e69be1246 · outbound

This paper cites A Continued Pretrained LLM Approach for Automatic Medical Note Generation.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs A Continued Pretrained LLM Approach for Automatic Medical Note Generation

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.752924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f61d649-5995-43be-bb31-8bcd1ae8d0cd · outbound

This paper cites TabuLa: Harnessing Language Models for Tabular Data Synthesis.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs TabuLa: Harnessing Language Models for Tabular Data Synthesis

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.757909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fe0946b-a029-47a1-a20b-6886c7009c93 · outbound

This paper cites Large Language Models for Disease Diagnosis: A Scoping Review.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Large Language Models for Disease Diagnosis: A Scoping Review

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.762839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation ed3f2462-810d-4561-8596-35ac654eef70 · inbound

Structured Semantics from Unstructured Notes: Language Model Approaches to EHR-Based Decision Support cites this paper.

Structured Semantics from Unstructured Notes: Language Model Approaches to EHR-Based Decision Support A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T12:00:31.577528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afabe818-e299-42cf-bdb4-52dc00778157 · inbound

Infherno: End-to-end Agent-based FHIR Resource Synthesis from Free-form Clinical Notes cites this paper.

Infherno: End-to-end Agent-based FHIR Resource Synthesis from Free-form Clinical Notes A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-19T04:37:04.048702Z

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 91965739-6775-49c3-8237-4780f800b19a · inbound

Uncertainty-Aware Foundation Models for Clinical Data cites this paper.

Uncertainty-Aware Foundation Models for Clinical Data A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:02.991722Z

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 b809f599-b1a4-4632-a5f7-f53e6e25f76f · inbound

Expanders Meet Reed-Muller: Easy Instances of Noisy k-XOR cites this paper.

Expanders Meet Reed-Muller: Easy Instances of Noisy k-XOR A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs

Reference 53

Resolution: unresolved; no resolver link, observed 2026-07-13T11:03:20.369425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 692049f1-d7b7-469a-a2c0-a4a29fc8f11f · inbound

Event Fields: Learning Latent Event Structure for Waveform Foundation Models cites this paper.

Event Fields: Learning Latent Event Structure for Waveform Foundation Models A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:06:32.141246Z

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 a41adcb8-f766-464c-8932-63907c9b2d43 · inbound

MorphologyFM: A Foundation Model for Morphology-Aware Representation Learning from ECG and Pulse Oximetry Waveforms cites this paper.

MorphologyFM: A Foundation Model for Morphology-Aware Representation Learning from ECG and Pulse Oximetry Waveforms A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs

Reference 45

Resolution: unresolved; no resolver link, observed 2026-07-14T16:31:09.661787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.