Paper Citation Record · LEDGER

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach

As of 9 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2607.03676.

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

pith.paper-citation-record.v1
2607.03676 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T00:45:43.255933Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

30 of 30 outbound references displayed

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unresolved30
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malformed identifier0
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External citation measurements

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Outbound references

Observation f1e2c16c-5306-4deb-bf52-c7a6105e18d6 · outbound

This paper cites Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating: Springer Science & Business Media; 2008.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating: Springer Science & Business Media; 2008

Reference 1

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Observation 2b6074c3-a537-4e15-8bf1-6c3758e879ad · outbound

This paper cites Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement

Reference 2

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Observation 1d2fa8ae-d519-41c6-bd10-e77f1b58643b · outbound

This paper cites TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

Reference 3

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Observation 16eb32ad-f72e-4598-8f25-96b537766895 · outbound

This paper cites Prognosis and prognostic research: application and impact of prognostic models in clinical practice.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Prognosis and prognostic research: application and impact of prognostic models in clinical practice

Reference 4

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Observation 942604e3-b641-4ddd-a0d5-aca285912006 · outbound

This paper cites Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research

Reference 5

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Observation 3f30d99a-414c-4443-b334-fa9ffa7158d7 · outbound

This paper cites Clinical Prediction Rules That Don't Hold Up—Where to Go From Here? Journal of Orthopaedic & Sports Physical Therapy.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Clinical Prediction Rules That Don't Hold Up—Where to Go From Here? Journal of Orthopaedic & Sports Physical Therapy

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-12T00:45:43.255933Z

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Observation 7e878682-86f2-4843-a058-5024f40cf191 · outbound

This paper cites External Validations of Cardiovascular Clinical Prediction Models: A Large-Scale Review of the Literature.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach External Validations of Cardiovascular Clinical Prediction Models: A Large-Scale Review of the Literature

Reference 7

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Observation e448f193-814b-4ecb-8034-afca02d0c639 · outbound

This paper cites All models are wrong and yours are useless: making clinical prediction models impactful for patients.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach All models are wrong and yours are useless: making clinical prediction models impactful for patients

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-12T00:45:43.255933Z

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Observation e4310f14-e425-4161-b958-a3ea07f8a770 · outbound

This paper cites A systematic review finds prediction models for chronic kidney disease were poorly reported and often developed using inappropriate methods.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach A systematic review finds prediction models for chronic kidney disease were poorly reported and often developed using inappropriate methods

Reference 9

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Observation c9fcc506-9bd4-42a9-b3ef-50e107c7ff9f · outbound

This paper cites Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review

Reference 10

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Observation d228251e-1a23-4383-a111-7f48f7ae0485 · outbound

This paper cites Does poor methodological quality of prediction modeling studies translate to poor model performance? An illustration in traumatic brain injury.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Does poor methodological quality of prediction modeling studies translate to poor model performance? An illustration in traumatic brain injury

Reference 11

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Observation 20786447-a1ef-498e-b068-e21778d70875 · outbound

This paper cites Methodological and applicability pitfalls of clinical prediction models for asthma diagnosis: a systematic review and critical appraisal of evidence.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Methodological and applicability pitfalls of clinical prediction models for asthma diagnosis: a systematic review and critical appraisal of evidence

Reference 12

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Observation 08a17d02-e752-4d8b-acdb-2c1b996e9c03 · outbound

This paper cites Stability of clinical prediction models developed using statistical or machine learning methods.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Stability of clinical prediction models developed using statistical or machine learning methods

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-12T00:45:43.255933Z

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Observation 66045c39-0f8f-49f9-8b24-cb4ad2c3c1e2 · outbound

This paper cites Developing clinical prediction models: a step-by-step guide.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Developing clinical prediction models: a step-by-step guide

Reference 14

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Observation d82c4f86-cceb-45b7-86df-947714138662 · outbound

This paper cites Internal validation of predictive models: Efficiency of some procedures for logistic regression analysis.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Internal validation of predictive models: Efficiency of some procedures for logistic regression analysis

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-12T00:45:43.255933Z

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Observation 4a4324e9-d9ca-41b5-b7fa-bc963c32b34f · outbound

This paper cites Events per variable (EPV) and the relative performance of different strategies for estimating the out-of-sample validity of logistic regression models.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Events per variable (EPV) and the relative performance of different strategies for estimating the out-of-sample validity of logistic regression models

Reference 16

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Observation 67cbe8a4-4169-4ec7-939a-5746c857a694 · outbound

This paper cites an unresolved cited work.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Unresolved cited work

Reference 17

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Observation 258ed4a3-2533-4e84-8186-4bd875401ac0 · outbound

This paper cites Empirical evaluation of internal validation methods for prediction in large-scale clinical data with rare-event outcomes: a case study in suicide risk prediction.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Empirical evaluation of internal validation methods for prediction in large-scale clinical data with rare-event outcomes: a case study in suicide risk prediction

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-12T00:45:43.255933Z

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Observation 4f6c49ac-bd73-4d9b-be92-a93e6f40395c · outbound

This paper cites Empirical simulation of internal validation methods for prediction models: comparing k-fold cross-validation with bootstrap- based optimism correction.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Empirical simulation of internal validation methods for prediction models: comparing k-fold cross-validation with bootstrap- based optimism correction

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-12T00:45:43.255933Z

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Observation 0cca5797-f2d0-4b8c-bbbe-6496ce3df22b · outbound

This paper cites A Guide to Cross-Validation for Artificial Intelligence in Medical Imaging.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach A Guide to Cross-Validation for Artificial Intelligence in Medical Imaging

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-12T00:45:43.255933Z

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Observation 1e38ec4f-7392-4e59-8d1b-a504164c1c0a · outbound

This paper cites Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-12T00:45:43.255933Z

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Observation 6fdd3a4c-0a6f-416c-8b14-c85174dbd914 · outbound

This paper cites Clinical prediction models and the multiverse of madness.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Clinical prediction models and the multiverse of madness

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-12T00:45:43.255933Z

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Observation f2dff378-dcf4-46bf-bef5-5891f81f909d · outbound

This paper cites PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-12T00:45:43.255933Z

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Observation 05f9c840-77eb-4a67-a772-88cfc89de637 · outbound

This paper cites Stability of clinical prediction models developed using statistical or machine learning methods.

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-12T00:45:43.255933Z

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Observation 705af1c9-f9ce-4b32-822e-b19da884815f · outbound

This paper cites Improvements on cross-validation: the .632+ bootstrap method.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Improvements on cross-validation: the .632+ bootstrap method

Reference 25

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Observation 0925a7de-2dab-4262-9573-db8e6820b28c · outbound

This paper cites Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI); 1995; San Francisco: Morgan Kaufmann.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI); 1995; San Francisco: Morgan Kaufmann

Reference 26

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Observation e33c97e9-095e-4b87-993b-8a7cfc94c5d2 · outbound

This paper cites Prediction error estimation: a comparison of resampling methods.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Prediction error estimation: a comparison of resampling methods

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-12T00:45:43.255933Z

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Observation c90c3f99-b28c-4d64-9ccd-db7f36f2ea2e · outbound

This paper cites Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-12T00:45:43.255933Z

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Observation b8d01e2a-e50b-4f1b-a6ab-828f212b989d · outbound

This paper cites Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-12T00:45:43.255933Z

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Observation 7cbd4d3d-937a-45ad-b5c9-d0e28e52a3f7 · outbound

This paper cites Calibration: the Achilles heel of predictive analytics.

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach Calibration: the Achilles heel of predictive analytics

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-12T00:45:43.255933Z

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