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Paper Citation Record · LEDGER

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography

As of 7 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2507.09009.

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

pith.paper-citation-record.v1
2507.09009 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:14:07.605907Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+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

26 of 26 outbound references displayed

  • verified exact4
  • verified fuzzy19
  • unresolved1
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 02e10c94-7a44-4ec4-acdd-4426c60bb9e4 · outbound

This paper cites Prospective study of the association between sleep-disordered breathing and hypertension.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography Prospective study of the association between sleep-disordered breathing and hypertension

Reference 1

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 0d3cdc7e-152b-41f3-8607-644bbac9f9d3 · outbound

This paper cites Sleep duration and cardiovascular disease risk: epidemiologic and experimental evidence.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography Sleep duration and cardiovascular disease risk: epidemiologic and experimental evidence

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation dc7170e7-91da-4f5e-ba75-52e821b7b4b1 · outbound

This paper cites Insomnia and incident cardiovascular disease: the Penn State Cohort.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography Insomnia and incident cardiovascular disease: the Penn State Cohort

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 876d71d6-4584-4d90-b3c8-a39c301dd573 · outbound

This paper cites an unresolved cited work.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography Unresolved cited work

Reference 4

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unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 35f24140-20cc-4556-916d-525490a550d4 · outbound

This paper cites Association of objectively measured sleep characteristics and hypertension: analysis from the Multi -Ethnic Study of Atherosclerosis (MESA).

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography Association of objectively measured sleep characteristics and hypertension: analysis from the Multi -Ethnic Study of Atherosclerosis (MESA)

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 946e7f37-ea18-45eb-af0d-de55fcc04238 · outbound

This paper cites Associations of sleep duration, sleep architecture and blood pressure dipping in middle-aged adults.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography Associations of sleep duration, sleep architecture and blood pressure dipping in middle-aged adults

Reference 6

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raw_fallback, observed 2026-08-06T18:14:08.189052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 362690f5-e810-4d6f-9b66-b4f834c59af1 · outbound

This paper cites Sleep-disordered breathing and cardiovascular disease: cross- sectional results of the Sleep Heart Health Study.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography Sleep-disordered breathing and cardiovascular disease: cross- sectional results of the Sleep Heart Health Study

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation cd588dce-913f-492d-9bcb-fd81c08486d4 · outbound

This paper cites Slow-wave sleep is associated with incident hypertension: the sleep heart health study.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography Slow-wave sleep is associated with incident hypertension: the sleep heart health study

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 2cba0b40-0bb0-4175-8aef-768749af4690 · outbound

This paper cites Prediction of Incident Atrial Fibrillation Using Handheld Single -Lead Electrocardiograms from the VITAL -AF Trial.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography Prediction of Incident Atrial Fibrillation Using Handheld Single -Lead Electrocardiograms from the VITAL -AF Trial

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 6be2e70c-b05e-4871-b4a9-9e540e5176dc · outbound

This paper cites A deep learning digital biomarker to detect hypertension and stratify cardiovascular risk from the electrocardiogram.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography A deep learning digital biomarker to detect hypertension and stratify cardiovascular risk from the electrocardiogram

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 608678dc-6944-41ba-9e00-4006c4d2d249 · outbound

This paper cites Electrocardiogram -based artificial intelligence to identify prevalent coronary artery disease.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography Electrocardiogram -based artificial intelligence to identify prevalent coronary artery disease

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 6adfc048-88e5-4a86-a9da-d27e760753b0 · outbound

This paper cites MTS -LOF: medical time -series representation learning via occlusion -invariant features.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography MTS -LOF: medical time -series representation learning via occlusion -invariant features

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 94c38e3c-a332-4659-872a-5783e3d32826 · outbound

This paper cites Self-supervised contrastive learning for medical time series: A systematic review.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography Self-supervised contrastive learning for medical time series: A systematic review

Reference 13

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Source-reported events for the cited work

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Observation ed58dc2d-4443-44b2-b3dc-0a21ddbd0638 · outbound

This paper cites SleepFM: multi -modal representation learning for sleep across brain activity, ECG and respiratory signals.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography SleepFM: multi -modal representation learning for sleep across brain activity, ECG and respiratory signals

Reference 14

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raw_fallback, observed 2026-08-06T18:14:08.042402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 6ba6b559-0f20-4535-b1d7-f03784d6c36a · outbound

This paper cites A foundational transformer leveraging full night, multichannel sleep study data accurately classifies sleep stages.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography A foundational transformer leveraging full night, multichannel sleep study data accurately classifies sleep stages

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation e3dc3fa4-e604-40d3-b65f-b819dfc1e4a5 · outbound

This paper cites A novel electroencephalogram-derived measure of disrupted delta wave activity during sleep predicts all-cause mortality risk.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography A novel electroencephalogram-derived measure of disrupted delta wave activity during sleep predicts all-cause mortality risk

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 5949f5c2-c8bb-4dc4-9f4a-90fa13196608 · outbound

This paper cites The hypoxic burden of sleep apnoea predicts cardiovascular disease-related mortality: the Osteoporotic Fractures in Men Study and the Sleep Heart Health Study.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography The hypoxic burden of sleep apnoea predicts cardiovascular disease-related mortality: the Osteoporotic Fractures in Men Study and the Sleep Heart Health Study

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 3c0e3cfe-795b-4d6a-bc68-1e60c4d5b0a0 · outbound

This paper cites Unsupervised deep learning of electrocardiograms enables scalable human disease profiling.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography Unsupervised deep learning of electrocardiograms enables scalable human disease profiling

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation e310baee-3e35-415c-9266-eb7046032ff2 · outbound

This paper cites Burden of sleep apnea: rationale, design, and major findings of the Wisconsin Sleep Cohort study.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography Burden of sleep apnea: rationale, design, and major findings of the Wisconsin Sleep Cohort study

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 5566be2e-2881-4b57-8909-19bd65e6b601 · outbound

This paper cites Segmentation of multivariate mixed data via lossy data coding and compression.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography Segmentation of multivariate mixed data via lossy data coding and compression

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation dfdd510a-3a3c-4c03-b4d7-cf3a090ae692 · outbound

This paper cites an unresolved cited work.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography Unresolved cited work

Reference 21

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verified exact
doi, observed 2026-08-06T18:14:07.687654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation af12e299-6084-4664-a8df-8e60df3f01be · outbound

This paper cites General cardiovascular risk profile for use in primary care: The Framingham heart study.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography General cardiovascular risk profile for use in primary care: The Framingham heart study

Reference 22

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verified exact
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Source-reported events for the cited work

correction dated 2008-07-21. Source: crossref record 10.1161/circulationaha.108.190154->10.1161/circulationaha.107.699579:correction, observed 2026-07-11T03:01:39.90381+00:00. This notice travels one citation hop only.

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Observation 52b5e8ce-b946-4887-9fe1-ac5d59a823b4 · outbound

This paper cites Understanding brain function in vascular cognitive impairment and dementia with EEG and MEG: A systematic review.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography Understanding brain function in vascular cognitive impairment and dementia with EEG and MEG: A systematic review

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 8b5c77ca-f316-4e04-8904-526c1326c17c · outbound

This paper cites Qualitative electroencephalogram and its predictors in the diagnosis of stroke.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography Qualitative electroencephalogram and its predictors in the diagnosis of stroke

Reference 24

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raw_fallback, observed 2026-08-06T18:14:07.861749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation e2692c0f-5a8a-468f-9cde-61587c5934e9 · outbound

This paper cites Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient

Reference 25

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doi, observed 2026-08-06T18:14:07.657132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 11a28677-a2a7-4b2f-ad22-c4815b0c7fd4 · outbound

This paper cites Baseline.

Multimodal Cardiovascular Risk Profiling Using Self-Supervised Learning of Polysomnography Baseline

Reference 26

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verified exact
doi, observed 2026-08-06T18:14:07.641430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Pith citing papers

No inbound Pith citation observations are available.