Paper Citation Record · LEDGER

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals

As of 8 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2507.19567.

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

pith.paper-citation-record.v1
2507.19567 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:22:39.766303Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

33 of 33 outbound references displayed

verified exact16
verified fuzzy3
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c57408c1-9eae-48dc-8659-7629a72d48a4 · outbound

This paper cites Artificial Intelligence: A Modern Approach.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Artificial Intelligence: A Modern Approach

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:22:43.562904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 083ec92f-2a5a-4c2c-ae1b-ba284381bd03 · outbound

This paper cites Predicting increased blood pressure using Machine Learning.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Predicting increased blood pressure using Machine Learning

Reference 2

Resolution: verified exact; doi, observed 2026-08-06T14:22:42.409086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c43a927f-8658-472c-8030-075d83d53481 · outbound

This paper cites Attention Is All You Need.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Attention Is All You Need

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T14:22:38.017802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0906f34-817e-4507-a72a-b235c973f90a · outbound

This paper cites an unresolved cited work.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:22:43.360365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 689b9386-9c57-45e6-b370-d5de0c9839ab · outbound

This paper cites Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T14:22:38.107976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50e72af3-d711-4eb9-affb-fbf8a0652cfc · outbound

This paper cites Scalable Extraction of Training Data from (Production) Language Models.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Scalable Extraction of Training Data from (Production) Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T14:22:38.174642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6cc971b-6990-4a4d-bb36-32a0697f54b7 · outbound

This paper cites Stealing Part of a Production Language Model.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Stealing Part of a Production Language Model

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T14:22:38.225032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a925ab86-0a2c-405b-bdea-6b186342873c · outbound

This paper cites Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attack.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attack

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T14:22:38.273725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5820c539-f8bd-4582-818b-9fedb9829479 · outbound

This paper cites Avianca, Inc.(United States District Court, Southern District of New York 2023).

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Avianca, Inc.(United States District Court, Southern District of New York 2023)

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:22:43.164079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 264cb868-6513-473d-beaa-bdc9d6a6c67f · outbound

This paper cites Artificial Hallucinations in ChatGPT: Implications in Scientific Writing.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Artificial Hallucinations in ChatGPT: Implications in Scientific Writing

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T14:22:38.429459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3009117d-880a-48b6-88ca-9ce0a703af7f · outbound

This paper cites GeneGPT: augmenting large language models with domain tools for improved access to biomedical information.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals GeneGPT: augmenting large language models with domain tools for improved access to biomedical information

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T14:22:38.497407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48abe657-eb31-4951-8f90-de7119b574b9 · outbound

This paper cites Hallucination is Inevitable: An Innate Limitation of Large Language Models.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Hallucination is Inevitable: An Innate Limitation of Large Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T14:22:38.546095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe8cf28c-5d02-4a17-b3e6-b1ba24cb8ffb · outbound

This paper cites Exposing Vulnerabilities in Clinical LLMs Through Data Poisoning Attacks: Case Study in Breast Cancer.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Exposing Vulnerabilities in Clinical LLMs Through Data Poisoning Attacks: Case Study in Breast Cancer

Reference 13

Resolution: verified exact; doi, observed 2026-08-06T14:22:42.107256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9c038059-3cb2-4575-b051-901eb6fa9de8 · outbound

This paper cites Poisoning scientific knowledge using large language models.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Poisoning scientific knowledge using large language models

Reference 14

Resolution: verified exact; doi, observed 2026-08-06T14:22:41.898957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d34990f5-2bd8-481c-b268-4e6303888663 · outbound

This paper cites Large language models propagate race-based medicine.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Large language models propagate race-based medicine

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T14:22:38.729961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2eb062fd-1990-4ccb-a50c-2f4eb4cf5416 · outbound

This paper cites A large language model-based generative natural language processing framework fine-tuned on clinical notes accurately extracts headache frequency from electronic health records.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals A large language model-based generative natural language processing framework fine-tuned on clinical notes accurately extracts headache frequency from electronic health records

Reference 16

Resolution: verified exact; doi, observed 2026-08-06T14:22:41.712839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation be329afc-d1a4-4278-9c8d-578e8cef6791 · outbound

This paper cites Large language models to identify social determinants of health in electronic health records.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Large language models to identify social determinants of health in electronic health records

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T14:22:38.822273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33a06a14-2f90-4151-ae71-6fdfb1a3ed32 · outbound

This paper cites Retrieval-augmented generation for knowledge- intensive NLP tasks.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Retrieval-augmented generation for knowledge- intensive NLP tasks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:22:42.929700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a59c2fa1-a579-48d3-b7ab-0b2619a1b921 · outbound

This paper cites Unveiling differential adverse event profiles in vaccines via LLM text embeddings and ontology semantic analysis.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Unveiling differential adverse event profiles in vaccines via LLM text embeddings and ontology semantic analysis

Reference 19

Resolution: verified exact; doi, observed 2026-08-06T14:22:41.547798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 46c3696f-7da3-4020-947c-d5badbed79fa · outbound

This paper cites Dual retrieving and ranking medical large language model with retrieval augmented generation.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Dual retrieving and ranking medical large language model with retrieval augmented generation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T14:22:38.965991Z

Source-reported events for the cited work

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Observation c80e0493-7551-4df9-b45a-d42ef4638664 · outbound

This paper cites Empowering PET imaging reporting with retrieval-augmented large language models and reading reports database: a pilot single center study.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Empowering PET imaging reporting with retrieval-augmented large language models and reading reports database: a pilot single center study

Reference 21

Resolution: verified exact; doi, observed 2026-08-06T14:22:41.409359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e3746725-51cf-4e08-8769-8e69bf6a1a62 · outbound

This paper cites GICL: A Cross-Modal Drug Property Prediction Framework Based on Knowledge Enhancement of Large Language Models.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals GICL: A Cross-Modal Drug Property Prediction Framework Based on Knowledge Enhancement of Large Language Models

Reference 22

Resolution: verified exact; doi, observed 2026-08-06T14:22:41.284002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4290156c-1e8e-4eff-9df7-1621ec5f162d · outbound

This paper cites CDEMapper: enhancing National Institutes of Health common data element use with large language models.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals CDEMapper: enhancing National Institutes of Health common data element use with large language models

Reference 23

Resolution: verified exact; doi, observed 2026-08-06T14:22:41.134882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 246d094c-f416-4655-8494-202642ea3614 · outbound

This paper cites ChatGPT usage in the Reactome curation process.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals ChatGPT usage in the Reactome curation process

Reference 24

Resolution: verified exact; doi, observed 2026-08-06T14:22:41.004872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 321de652-359e-4428-9212-9b0e61b71eeb · outbound

This paper cites The application of ChatGPT in healthcare progress notes: A commentary from a clinical and research perspective.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals The application of ChatGPT in healthcare progress notes: A commentary from a clinical and research perspective

Reference 25

Resolution: verified exact; doi, observed 2026-08-06T14:22:40.832309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f4cf4eab-8da1-4709-bdfd-cfee768d6cfe · outbound

This paper cites Semantic search using protein large language models detects class II microcins in bacterial genomes.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Semantic search using protein large language models detects class II microcins in bacterial genomes

Reference 26

Resolution: verified exact; doi, observed 2026-08-06T14:22:40.720031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0b28668f-746e-4ca9-8b62-4ab340f61910 · outbound

This paper cites Deep embedding and alignment of protein sequences.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Deep embedding and alignment of protein sequences

Reference 27

Resolution: verified exact; doi, observed 2026-08-06T14:22:40.556866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 709d4193-a61b-4b4a-a82e-ea4c92c8bbee · outbound

This paper cites Automated design of protein-binding riboswitches for sensing human biomarkers in a cell-free expression system.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Automated design of protein-binding riboswitches for sensing human biomarkers in a cell-free expression system

Reference 28

Resolution: verified exact; doi, observed 2026-08-06T14:22:40.404169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 710080b4-06ed-427d-9dc8-1e28b659013c · outbound

This paper cites Predicting Splicing from Primary Sequence with Deep Learning.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Predicting Splicing from Primary Sequence with Deep Learning

Reference 29

Resolution: verified exact; doi, observed 2026-08-06T14:22:40.250017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9d433540-3f01-48c9-9fb0-e81ca9a2a597 · outbound

This paper cites RUDEUS, a machine learning classification system to study DNA-Binding proteins.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals RUDEUS, a machine learning classification system to study DNA-Binding proteins

Reference 30

Resolution: verified exact; doi, observed 2026-08-06T14:22:40.093983Z

Source-reported events for the cited work

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Observation 40404646-9c17-48e5-a065-55e1642452dc · outbound

This paper cites TemBERTure: Advancing protein thermostability prediction with Deep Learning and attention mechanisms.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals TemBERTure: Advancing protein thermostability prediction with Deep Learning and attention mechanisms

Reference 31

Resolution: verified exact; doi, observed 2026-08-06T14:22:39.895728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c69e57d3-a344-4030-b27b-0097a77af4d6 · outbound

This paper cites Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T14:22:39.709913Z

Source-reported events for the cited work

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Observation e1370769-ec9f-4a0c-8cb2-e4cbf228bf00 · outbound

This paper cites Evaluation of ChatGPT-generated medical responses: A systematic review and meta-analysis.

Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals Evaluation of ChatGPT-generated medical responses: A systematic review and meta-analysis

Reference 33

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T14:22:42.682565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.