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July 13, 2026Open Access

Automated Peer Review Quality Prediction: Can ML Identify Accept/Reject Outcomes Before Submission?

Олег Ивченко Iryna Ivchenko

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Key Points

The aim is to explore whether machine learning can accurately predict the outcomes of peer review processes before article submission.
Utilized machine learning algorithms to analyze prior peer review outcomes.
Developed a predictive model to assess accept/reject outcomes based on submission data.
Tested the model with previously reviewed articles for validation.
The model successfully predicted accept/reject outcomes with an accuracy of 85%.
Sensitivity for accept predictions was 78%, and specificity for reject predictions was 82%.
The findings suggest that using machine learning could streamline peer review processes.

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Abstract

Research article: Automated Peer Review Quality Prediction: Can ML Identify Accept/Reject Outcomes Before Submission?

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Cite This Study

Ивченко et al. (2026) studied this question.

synapsesocial.com/papers/6a55d16e5aafca87247f867f https://doi.org/10.5281/zenodo.21328364

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