Dear Dr. Abdelhay,

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Hamed Tavolinejad

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Does the manuscript provide a valid rationale for the proposed study, with clearly identified and justified research questions?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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2. Is the protocol technically sound and planned in a manner that will lead to a meaningful outcome and allow testing the stated hypotheses??>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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3. Is the methodology feasible and described in sufficient detail to allow the work to be replicable??>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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4. Have the authors described where all data underlying the findings will be made available when the study is complete??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Please use the space provided to explain your answers to the questions above and, if applicable, provide comments about issues authors must address before this protocol can be accepted for publication. You may also include additional comments for the author, including concerns about research or publication ethics.

You may also provide optional suggestions and comments to authors that they might find helpful in planning their study.

Reviewer #1: This is an exemplary and timely study protocol that addresses the critical methodological challenge of class imbalance in clinical prediction models. The authors have proposed an exceptionally rigorous and transparent plan, adhering to the highest standards for systematic reviews (e.g., PRISMA-P/ScR, INPLASY pre-registration). The comprehensive search strategy and robust plan for a dual descriptive and quantitative synthesis are major strengths.

The protocol is nearly ready for publication. I have only a few minor suggestions to enhance its clarity and precision before it is finalized:

Clarity on Calibration Metrics: The protocol mentions collecting calibration slope and Brier score. To strengthen the pre-specified analysis plan, please consider explicitly stating how these metrics will be synthesized (e.g., will it be a descriptive summary only, or will they be pooled if sufficient homogeneity is found?).

Justification for Scope Exclusion: The exclusion of radiomics and image-segmentation tasks is a sensible scoping decision. A brief justification in the eligibility criteria section (e.g., clarifying that pixel-level imbalance is conceptually different from the patient-level focus of this review) would strengthen the manuscript.

Terminology Consistency: For consistency, I recommend standardizing terms. For example, "resampling class" and "resampling strategy" are both used; standardizing to one term would be ideal. Similarly, please clarify if "small-study effects" and "publication bias" will be reported as distinct concepts or if one term will be used to encompass the analysis.

This is an outstanding protocol for a review that will be a significant contribution to the field of medical AI. I commend the authors on their meticulous work and look forward to seeing the results of the completed study. With the minor clarifications outlined above, I believe this protocol will be ready for publication.

Reviewer #2: Overview:

The manuscript addresses a critical challenge in clinical prediction modeling: managing class imbalance in datasets. This is highly relevant, as rare outcomes are common in healthcare and can lead to biased or underperforming predictive models if not properly addressed. The manuscript provides a clear overview of strategies such as resampling, algorithm-level adjustments, and synthetic data generation.

Strengths of the clinical report:

The topic is timely and clinically important.

Provides a comprehensive review of methods for handling class imbalance.

Well-organized and clearly presented, making complex concepts accessible.

Major Concerns:

Evaluation Metrics: The manuscript should discuss metrics better suited for imbalanced data, such as precision-recall curves, F1 score, and Matthews correlation coefficient, as accuracy alone may be misleading.

Clinical Relevance: Including specific clinical examples or datasets would make the discussion more concrete and applicable to real-world scenarios.

Methodological Transparency: If novel methods or experimental comparisons are presented, more detail on preprocessing, parameter choices, and implementation is needed to allow reproducibility.

Ethical Considerations: Briefly addressing patient privacy, potential biases, and data-sharing limitations would strengthen the manuscript.

Suggestions for Improvement:

Include a summary table of methods with pros, cons, and clinical applicability.

Discuss trade-offs of oversampling, undersampling, and synthetic data approaches, including risks like overfitting.

Consider adding a brief section on emerging approaches such as ensemble learning or transfer learning for imbalanced clinical datasets.

Reviewer #3: Review Comments to the Author (Major Revisions Suggested):

(a) Clarify Handling of Calibration and Cost-Sensitive Metrics

While AUC is the chosen primary outcome, calibration and cost-sensitive performance are critical in imbalanced clinical datasets. At present, you acknowledge under-reporting but do not specify how such incomplete reporting will be addressed in synthesis. Please outline in more detail whether you plan descriptive-only mapping, imputation strategies, or sensitivity analyses when calibration/misclassification costs are missing.

(b) Address Heterogeneity in Meta-Regression

The anticipated heterogeneity across clinical domains, imbalance ratios, and study designs is very high. Although you plan random-effects meta-regression, the current description does not clarify how you will deal with extremely sparse subgroups or high I² values. Please expand on thresholds for deciding when quantitative synthesis is inappropriate, and what fallback narrative synthesis strategy you will adopt.

(c) Define Inclusion/Exclusion for Grey Literature

The search strategy includes medRxiv, arXiv, bioRxiv, and GitHub. These sources often contain incomplete or non–peer reviewed manuscripts. Please provide stricter criteria (e.g., methodological completeness, minimum reporting standards) for including such grey-literature studies in synthesis to ensure transparency and quality.

(d) Clarify Treatment of Duplicates and Overlapping Data

Given multiple publications may analyze overlapping datasets (e.g., the same hospital EHR in preprints and journals), please specify how you will identify and handle duplicate or overlapping cohorts to avoid double counting.

(e) Protocol Transparency and Amendments

Although registered in INPLASY, the manuscript should provide more detail on how protocol amendments (e.g., changing inclusion criteria, new statistical models) will be documented and justified. Please strengthen this section for reproducibility.

(f) Consider Broader Performance Metrics Beyond AUC

AUC is threshold-independent but often criticized in imbalanced data. Since your secondary outcomes include sensitivity, specificity, F1, and calibration, you should justify why AUC was prioritized and describe how threshold-dependent metrics will be weighted in interpretation.

(g) Improve Readability for Non-Methodological Readers

Some sections (especially the statistical analysis plan) are dense and jargon-heavy. Please consider simplifying key explanations or moving highly technical details (e.g., restricted cubic splines, influence diagnostics) to supplementary materials, while keeping the main text more accessible.

(h) Risk of Bias and Quality Assessment

The authors state that study-level bias tools like PROBAST are “optional” because this is a methodological review. However, lack of structured bias assessment is a significant weakness. Even methodological studies can vary in reporting quality, data leakage, or reproducibility. At minimum, they should commit to applying a tailored bias/quality checklist to ensure study validity.

(i) Unclear Hypothesis Formulation

Although the aims are well listed, the protocol does not clearly articulate testable hypotheses (e.g., “cost-sensitive learning performs better than oversampling under X imbalance ratios”). Instead, it states broad objectives. For a study planning meta-regression, more explicit hypotheses would improve methodological coherence.

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Reviewer #1: No

Reviewer #2: Yes:  Adekunle Adeoye

Reviewer #3: Yes:  Shake Ibna Abir

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Resampling Methods for Class Imbalance in Clinical Prediction Models: A Scoping Review Protocol

PONE-D-25-36127R1

Dear Dr. Abdelhay,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Hamed Tavolinejad, MD

Academic Editor

PLOS ONE