Paper Citation Record · LEDGER

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits

As of 8 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2507.14079.

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

pith.paper-citation-record.v1
2507.14079 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:13:19.288298Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

36 of 36 outbound references displayed

verified exact6
verified fuzzy25
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 430f9fb9-79bf-46ff-b099-3b19a72f080c · outbound

This paper cites Characterizing the source of text in electronic health record progress notes,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Characterizing the source of text in electronic health record progress notes,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:25.495632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 118f5d6b-ad93-4c7f-804a-45e6bc9438f7 · outbound

This paper cites Learning to write case notes using the soap format,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Learning to write case notes using the soap format,

Reference 2

Resolution: verified exact; raw_fallback, observed 2026-08-06T16:13:20.146054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f34e2de9-f037-4f8d-89f4-28203b3c63e0 · outbound

This paper cites Length and redundancy of outpatient progress notes across a decade at an academic medical center,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Length and redundancy of outpatient progress notes across a decade at an academic medical center,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:25.228320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0745a5be-47bd-465b-afba-09f533c9dd96 · outbound

This paper cites Prediction of emergency department patient disposition based on natural language processing of triage notes,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Prediction of emergency department patient disposition based on natural language processing of triage notes,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:24.916939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 23ee28cd-f951-4380-b3f5-a25322fc6d98 · outbound

This paper cites Hierarchical annotation for building a suite of clinical natural language processing tasks: progress note understanding,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Hierarchical annotation for building a suite of clinical natural language processing tasks: progress note understanding,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:24.665368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 64324df0-bc11-4499-ab93-10ddb6401647 · outbound

This paper cites Leveraging medical knowledge graphs into large language models for diagnosis prediction: Design and application study,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Leveraging medical knowledge graphs into large language models for diagnosis prediction: Design and application study,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:24.474793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6ed64f41-2d5e-43e1-b493-99e6f54e003a · outbound

This paper cites Attention-based clinical note summarization,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Attention-based clinical note summarization,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:24.315555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ab16b021-66ca-4c41-8e83-4197677deded · outbound

This paper cites A multimodal transformer: Fusing clinical notes with structured ehr data for interpretable in-hospital mortality prediction,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits A multimodal transformer: Fusing clinical notes with structured ehr data for interpretable in-hospital mortality prediction,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:24.179616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 50004c0a-db05-4a45-8e61-eeec0cedbb61 · outbound

This paper cites Reducing redundancy in clinical documentation: a study of progress note content and structure,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Reducing redundancy in clinical documentation: a study of progress note content and structure,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:24.053515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d6b2518d-e146-4efd-af4a-e632c8bcdc82 · outbound

This paper cites Copy, paste, and cloned notes in electronic health records,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Copy, paste, and cloned notes in electronic health records,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:23.893226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fc8c773d-9b32-4502-a6d7-6eb6c3d18adf · outbound

This paper cites Clinical documentation in the 21st century: executive summary of a policy position paper from the american college of physicians,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Clinical documentation in the 21st century: executive summary of a policy position paper from the american college of physicians,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:23.740006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b8671252-92f5-4f6c-b368-6562f8424c9f · outbound

This paper cites Mimic-iii, a freely accessible critical care database,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Mimic-iii, a freely accessible critical care database,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T16:13:17.165721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a7d0f15-2264-4cc7-b530-3af122483c7c · outbound

This paper cites Redundancy of progress notes for serial office visits,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Redundancy of progress notes for serial office visits,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:23.611899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1f2408b2-1651-49c3-8d2c-19610d3b223b · outbound

This paper cites CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T16:13:17.389506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94c49fc7-4381-4f32-a536-486c02e1eea6 · outbound

This paper cites Are synthetic clinical notes useful for real natural language processing tasks: A case study on clinical entity recognition,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Are synthetic clinical notes useful for real natural language processing tasks: A case study on clinical entity recognition,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:23.334397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6141f284-bee4-4b9c-a94b-0da02ae9df6e · outbound

This paper cites Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language Models.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language Models

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:13:19.956241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f76e92c7-4f6d-4aad-b92d-0df98de95f97 · outbound

This paper cites Progress Notes Classification and Keyword Extraction using Attention-based Deep Learning Models with BERT.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Progress Notes Classification and Keyword Extraction using Attention-based Deep Learning Models with BERT

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:13:19.831236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e67969b7-729a-467e-8dbe-9e063948dcad · outbound

This paper cites Toward relieving clinician burden by automatically generating progress notes using interim hospital data,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Toward relieving clinician burden by automatically generating progress notes using interim hospital data,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:22.555669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bf7c0d93-e9a7-4aea-81d2-b16d9303f7dd · outbound

This paper cites Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T16:13:17.650376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d77ff64e-b725-4b45-86b0-aecea346c057 · outbound

This paper cites Clinicalt5: A generative language model for clinical text,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Clinicalt5: A generative language model for clinical text,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:22.067974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 75087547-b3ad-488c-8586-dd4b3ececd7f · outbound

This paper cites Assessing electronic note quality using the physician documentation quality instru- ment (pdqi-9),.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Assessing electronic note quality using the physician documentation quality instru- ment (pdqi-9),

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:21.892243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bd835e87-2033-4658-9607-3ab0c4eaed63 · outbound

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

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:21.815509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 51db5334-e370-4353-845e-6e1a456d27ed · outbound

This paper cites Advancing informatics with electronic medical records bots (emrbots),.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Advancing informatics with electronic medical records bots (emrbots),

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:21.688746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3d600dba-43cd-40cd-a6dc-a20b54c02207 · outbound

This paper cites Gen- erating multi-label discrete patient records using generative adversarial networks,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Gen- erating multi-label discrete patient records using generative adversarial networks,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T16:13:18.248401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9a203b6-d7fd-4434-9ab5-7140e263b176 · outbound

This paper cites ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T16:13:18.348427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcd89506-655f-415b-a5ce-29cf16720bc6 · outbound

This paper cites Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:13:19.672387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 01e29cfd-c2ff-4072-afd4-82685f466bc4 · outbound

This paper cites The Dynamic Embedded Topic Model.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits The Dynamic Embedded Topic Model

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:13:19.579687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b1f210e8-46aa-4037-9e6e-309c5c1315d1 · outbound

This paper cites Etm: Enrichment by topic modeling for automated clin- ical sentence classification to detect patients’ disease history,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Etm: Enrichment by topic modeling for automated clin- ical sentence classification to detect patients’ disease history,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:21.482413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b10fdac9-effe-4fee-a266-62abf8e0a62a · outbound

This paper cites Dynamic topic models,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Dynamic topic models,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:21.350465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6961c0b8-4e68-48fc-b35f-2deaabe679a8 · outbound

This paper cites A systematic review of large language model (llm) evaluations in clinical medicine,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits A systematic review of large language model (llm) evaluations in clinical medicine,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:21.174911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c4a05adf-ff83-455b-b2cf-d4261111c004 · outbound

This paper cites Evaluating measures of redundancy in clinical texts,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Evaluating measures of redundancy in clinical texts,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:20.960282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d57b0b56-13e3-4fed-b1ff-3ba415d00e4d · outbound

This paper cites Quantifying clinical narrative redundancy in an electronic health record,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Quantifying clinical narrative redundancy in an electronic health record,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:20.761145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f643500a-6f3a-4eb6-b5a1-c676f63b7caa · outbound

This paper cites “note bloat.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits “note bloat

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:20.530702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d3fbfb06-1d65-4c06-9b78-595afd14d31c · outbound

This paper cites Modeling local coherence: An entity-based approach,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Modeling local coherence: An entity-based approach,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:20.368312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bd33d576-c72b-44c4-bd23-e2f3bed494d0 · outbound

This paper cites Neural net models of open-domain discourse coherence,.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Neural net models of open-domain discourse coherence,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:13:20.246180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6ef2f7f2-3e54-4282-9a1e-41f6c859800b · outbound

This paper cites Assessing the Quality of AI-Generated Clinical Notes: A Validated Evaluation of a Large Language Model Scribe.

DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits Assessing the Quality of AI-Generated Clinical Notes: A Validated Evaluation of a Large Language Model Scribe

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:13:19.463456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.