Research Opportunity

This systematic review and meta-analysis examines the explainability frameworks, methodological rigor, and predictive performance of machine learning models for stroke outcome prediction. By synthesizing evidence from published studies, the review aims to evaluate model discrimination, calibration, validation strategies, reporting quality, and explainability methods to assess their reliability, transparency, and readiness for clinical implementation. 

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Authorship & Contribution Policy

Medical Research Spot follows the International Committee of Medical Journal Editors (ICMJE) recommendations.
Authorship is awarded only to contributors who fulfill all four ICMJE criteria, Medical Research Spot follows the ICMJE Recommendations for Defining the Role of Authors and Contributors.

including:

• Making a substantial scientific contribution to the project.
• Drafting or critically revising the manuscript.
• Approving the final manuscript before submission.
• Accepting responsibility for the integrity of the work.

Contributors who do not meet these requirements may be acknowledged but will not be listed as authors.
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Research Integrity

Medical Research Spot is committed to ethical, transparent, and responsible scientific research.

• Manuscripts are prepared in accordance with internationally recognized reporting guidelines appropriate to the study design (e.g., PRISMA, STROBE, CARE, and other EQUATOR guidelines where applicable).
• All manuscripts are submitted to independent peer-reviewed journals. Editorial decisions, peer review, acceptance, and indexing remain solely at the discretion of the journal.

Disclaimer: Participation in a project does not guarantee authorship, journal acceptance, publication, or indexing. All outcomes are subject to journal policies and editorial review.