Paper Citation Record · LEDGER

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data

As of 10 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2506.03209.

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

pith.paper-citation-record.v1
2506.03209 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:29:56.894761Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

39 of 39 outbound references displayed

verified exact24
verified fuzzy8
unresolved3
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4a163426-9cfa-43a5-a9df-d24c292d8c7e · outbound

This paper cites Pathophysiology and Treatment of Stroke: Present Status and Future Perspectives.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Pathophysiology and Treatment of Stroke: Present Status and Future Perspectives

Reference 1

Resolution: verified exact; doi, observed 2026-08-07T11:29:57.142467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4725daef-986d-444a-b5df-0cae2a4420aa · outbound

This paper cites Influence of age and health behaviors on stroke risk: Lessons from longitudinal studies.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Influence of age and health behaviors on stroke risk: Lessons from longitudinal studies

Reference 2

Resolution: verified exact; raw_fallback, observed 2026-08-07T11:29:57.732915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6c67ea11-7752-4a2a-b2b9-b31c03bda727 · outbound

This paper cites an unresolved cited work.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Unresolved cited work

Reference 3

Resolution: verified exact; doi, observed 2026-08-07T11:29:57.131869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 15fdbbee-c51f-4cc6-98c8-f2ee03a03935 · outbound

This paper cites Stroke, Cerebrovascular accident; n.d.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Stroke, Cerebrovascular accident; n.d

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:57.803702Z

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Observation f96d9f67-9ffc-42e0-b1e9-a510e142e6ed · outbound

This paper cites A contemporary and comprehensive analysis of the costs of stroke in the United States.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data A contemporary and comprehensive analysis of the costs of stroke in the United States

Reference 5

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T11:29:57.669967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 48ae8d79-08de-494e-b048-ae727aaf6458 · outbound

This paper cites Economic burden of stroke across Europe: A population-based cost analysis.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Economic burden of stroke across Europe: A population-based cost analysis

Reference 6

Resolution: verified exact; doi, observed 2026-08-07T11:29:57.120568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ea12d2f6-c5c4-4b26-9fd9-a50b3b589e78 · outbound

This paper cites Global, regional, and national burden of stroke, 1990-2016: A systematic analysis for the Global Burden of Disease Study 2016.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Global, regional, and national burden of stroke, 1990-2016: A systematic analysis for the Global Burden of Disease Study 2016

Reference 7

Resolution: verified exact; doi, observed 2026-08-07T11:29:57.111370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4da6665d-5bb5-4c7b-b3bd-213ae6af7eb5 · outbound

This paper cites Stroke risk factors, genetics, and prevention.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Stroke risk factors, genetics, and prevention

Reference 8

Resolution: verified exact; doi, observed 2026-08-07T11:29:57.102040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dc5e73de-23a2-4e02-a376-0983658c6702 · outbound

This paper cites Aging and ischemic stroke.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Aging and ischemic stroke

Reference 9

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Source-reported events for the cited work

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Observation ce753bcb-16f8-47cd-804d-184d3a3fd19c · outbound

This paper cites Stroke epidemiology: a review of population-based studies of incidence, prevalence, and case-fatality in the late 20th century.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Stroke epidemiology: a review of population-based studies of incidence, prevalence, and case-fatality in the late 20th century

Reference 10

Resolution: verified exact; doi, observed 2026-08-07T11:29:57.087253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e4a56a64-0b61-4eea-b0f7-ea151dc7cb02 · outbound

This paper cites Human and economic burden of stroke.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Human and economic burden of stroke

Reference 11

Resolution: verified exact; doi, observed 2026-08-07T11:29:57.079083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 825f2865-3a85-41b6-a424-c20264645097 · outbound

This paper cites Outcomes in perioperative care.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Outcomes in perioperative care

Reference 12

Resolution: verified exact; doi, observed 2026-08-07T11:29:57.070242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 60762235-a55b-4b98-93ff-5af8f55ae2d9 · outbound

This paper cites Impact of perioperative acute ischemic stroke on the outcomes of noncardiac and nonvascular surgery: a single centre prospective study.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Impact of perioperative acute ischemic stroke on the outcomes of noncardiac and nonvascular surgery: a single centre prospective study

Reference 13

Resolution: verified exact; doi, observed 2026-08-07T11:29:57.062206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e0e8aa70-0f83-4e19-9169-a94379f76b1c · outbound

This paper cites Risk Factors and Stroke Characteristic in Patients with Postoperative Strokes.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Risk Factors and Stroke Characteristic in Patients with Postoperative Strokes

Reference 14

Resolution: verified exact; doi, observed 2026-08-07T11:29:57.053256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b410b478-5702-46ad-bd0a-171656a3d59d · outbound

This paper cites Prevention of Stroke in Patients With Atrial Fibrillation: Anticoagulation Strategies and Beyond.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Prevention of Stroke in Patients With Atrial Fibrillation: Anticoagulation Strategies and Beyond

Reference 15

Resolution: verified exact; doi, observed 2026-08-07T11:29:57.043229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ec2099d3-3814-4300-83e4-856fc954d511 · outbound

This paper cites Stroke prevention: from primary prevention to early rehabilitation.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Stroke prevention: from primary prevention to early rehabilitation

Reference 16

Resolution: verified exact; raw_fallback, observed 2026-08-07T11:29:57.579670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 79291df8-0997-4881-bbbf-8aba9a03e203 · outbound

This paper cites Stroke declines from third to fourth leading cause of death in the United States: historical perspective and challenges ahead.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Stroke declines from third to fourth leading cause of death in the United States: historical perspective and challenges ahead

Reference 17

Resolution: verified exact; doi, observed 2026-08-07T11:29:57.034273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 35bbbef7-510b-4e35-8f0a-c089de2577b1 · outbound

This paper cites Global Aging and our Futures.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Global Aging and our Futures

Reference 18

Resolution: verified exact; raw_fallback, observed 2026-08-07T11:29:57.478899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 74cb6ec8-338e-417b-a2bd-51caf37c3b87 · outbound

This paper cites Predictors of early and late stroke following cardiac surgery.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Predictors of early and late stroke following cardiac surgery

Reference 19

Resolution: verified exact; doi, observed 2026-08-07T11:29:57.023825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 18ebfba6-741a-498f-bb9f-1d61456740ef · outbound

This paper cites Usefulness of the CHA2DS2VASc score to predict postoperative stroke in patients having cardiac surgery independent of atrial fibrillation.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Usefulness of the CHA2DS2VASc score to predict postoperative stroke in patients having cardiac surgery independent of atrial fibrillation

Reference 20

Resolution: verified exact; doi, observed 2026-08-07T11:29:57.014072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7d862dbf-916d-42ce-a383-82bf74794ecf · outbound

This paper cites Predicting mortality in Sepsis-Associated acute respiratory distress syndrome: A machine learning approach using the MIMIC-III database.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Predicting mortality in Sepsis-Associated acute respiratory distress syndrome: A machine learning approach using the MIMIC-III database

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:57.794385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 298382e4-be4f-4e81-9b89-2bb85978ea9b · outbound

This paper cites Predicting ICU Readmission in Acute Pancreatitis Patients Using a Machine Learning-Based Model with Enhanced Clinical Interpretability.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Predicting ICU Readmission in Acute Pancreatitis Patients Using a Machine Learning-Based Model with Enhanced Clinical Interpretability

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:57.786187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0bbbfaf2-df28-4db7-937d-119d8f8e1ab5 · outbound

This paper cites an unresolved cited work.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Unresolved cited work

Reference 23

Resolution: verified exact; doi, observed 2026-08-07T11:29:57.004823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e9c64109-ce63-4e6c-a416-a1b214c6057a · outbound

This paper cites Retrospective Machine Learning Approach for Forecasting In-Hospital Death in ICU Patients After Cardiac Arrest.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Retrospective Machine Learning Approach for Forecasting In-Hospital Death in ICU Patients After Cardiac Arrest

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:57.776801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6709799d-e0e8-460c-a55b-b1d31a7755e8 · outbound

This paper cites MIMIC-III, a freely accessible critical care database.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data MIMIC-III, a freely accessible critical care database

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T11:29:56.852126Z

Source-reported events for the cited work

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Observation 03c5ee0e-c835-49b3-8c39-eec86fa84c39 · outbound

This paper cites MIMIC-IV, a freely accessible electronic health record dataset.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data MIMIC-IV, a freely accessible electronic health record dataset

Reference 26

Resolution: verified exact; doi, observed 2026-08-07T11:29:56.988721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e5ca3647-77b7-46c3-bc68-eec594187ad4 · outbound

This paper cites Validity of International Classification of Disease Codes to Identify Ischemic Stroke and Intracranial Hemorrhage Among Individuals With Associated Diagnosis of Atrial Fibrillation.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Validity of International Classification of Disease Codes to Identify Ischemic Stroke and Intracranial Hemorrhage Among Individuals With Associated Diagnosis of Atrial Fibrillation

Reference 27

Resolution: verified exact; doi, observed 2026-08-07T11:29:56.978048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6c750861-144d-4313-ac02-087a5e55fcb7 · outbound

This paper cites Machine Learning Prediction Models for Postoperative Stroke in Elderly Patients: Analyses of the MIMIC Database.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Machine Learning Prediction Models for Postoperative Stroke in Elderly Patients: Analyses of the MIMIC Database

Reference 28

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T11:29:57.415654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0098ea34-8f46-4aa4-af63-5158fa1342d3 · outbound

This paper cites Normalization methods for clinical data in machine learning: A systematic review.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Normalization methods for clinical data in machine learning: A systematic review

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:57.768085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b1a83c96-1fe1-457b-9d01-23060abe9b5f · outbound

This paper cites The importance of the normality assumption in large public health data sets.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data The importance of the normality assumption in large public health data sets

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:57.759198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6446e27c-5906-4c32-a003-d8a6b7f095e1 · outbound

This paper cites Chi-square Test and its Application in Hypothesis Testing.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Chi-square Test and its Application in Hypothesis Testing

Reference 31

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T11:29:57.339226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a0abc15c-ab2b-49d3-bca8-7b5c904c0a5a · outbound

This paper cites Machine Learning–Based Model for Prediction of Outcomes in Acute Stroke.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Machine Learning–Based Model for Prediction of Outcomes in Acute Stroke

Reference 32

Resolution: verified exact; doi, observed 2026-08-07T11:29:56.966728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d46885a9-d408-4982-9df1-f5a6309dd893 · outbound

This paper cites Machine Learning for Brain Stroke: A Review.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Machine Learning for Brain Stroke: A Review

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T11:29:56.876153Z

Source-reported events for the cited work

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Observation cfd0e91c-6f33-414b-81da-e8cdb1bc2164 · outbound

This paper cites Stroke Prediction with Machine Learning Methods among Older Chinese.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Stroke Prediction with Machine Learning Methods among Older Chinese

Reference 34

Resolution: verified exact; doi, observed 2026-08-07T11:29:56.957472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7565efb8-606a-45d6-ae46-5385df299ff4 · outbound

This paper cites Perioperative Low Arterial Oxygenation Is Associated With Increased Stroke Risk in Cardiac Surgery.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Perioperative Low Arterial Oxygenation Is Associated With Increased Stroke Risk in Cardiac Surgery

Reference 35

Resolution: verified exact; doi, observed 2026-08-07T11:29:56.947344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7e3270d4-b109-409b-ad4a-4e4a7ebab64a · outbound

This paper cites A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation

Reference 36

Resolution: malformed identifier; doi_truncated, observed 2026-08-07T11:29:56.936400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a402968b-cb35-4c51-a5ca-a60215161025 · outbound

This paper cites A unified approach to interpreting model predictions.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data A unified approach to interpreting model predictions

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:57.750588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e2586a4b-b843-4fd8-8419-e23aeb9648ca · outbound

This paper cites ADASYN: Adaptive synthetic sampling approach for imbalanced learning.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data ADASYN: Adaptive synthetic sampling approach for imbalanced learning

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:29:57.741609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 61fda66d-aed2-4d52-9b47-b7749918d9f4 · outbound

This paper cites Development and validation of a prediction model for strokes after coronary artery bypass grafting.

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data Development and validation of a prediction model for strokes after coronary artery bypass grafting

Reference 39

Resolution: verified exact; doi, observed 2026-08-07T11:29:56.925335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.