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Fine-Tuning and Benchmarking Transformer Models for Multiclass Classification of Clinical Research Papers:

Fangwen Zhou¹, Cynthia Lokker¹, Rick Parrish¹

¹Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada.

|April 30, 2026

Summary

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This summary is machine-generated.

Fine-tuned transformer models, particularly BioBERT, excel at multiclass classification of clinical literature. Optimal hyperparameter tuning is key for robust performance in evidence synthesis and knowledge translation.

Area of Science:

Natural Language Processing
Machine Learning in Healthcare
Biomedical Informatics

Background:

The rapid growth of digital health information necessitates efficient text classification.
Multiclass classification of study types is crucial for evidence synthesis but remains under-explored.
Transformer models offer promise for enhancing knowledge translation workflows.

Purpose of the Study:

To fine-tune and evaluate domain-specific transformer models for multiclass classification of clinical literature.
To categorize papers into original studies, reviews, guidelines, and nonexperimental studies.
To identify optimal model configurations for accurate literature classification.

Main Methods:

Fine-tuned seven transformer models on the McMaster PLUS dataset (162,380 papers).

Keywords:

classification deep learning information science medical informatics natural language processing

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Utilized a comprehensive grid search (1890 configurations) for hyperparameter optimization (class weight, learning rate, batch size, etc.).

Assessed models using 10 metrics, including AUROC, F1-score, and MCC, with external validation on the Clinical Hedges dataset.

Main Results:

Top models achieved macro AUROC ≥0.99, F1-score ≥0.89, and MCC ≥0.88.
BioBERT-based models demonstrated superior calibration, especially for original studies and reviews.
Models struggled with nonexperimental and guideline studies, likely due to class imbalance and heterogeneity.

Conclusions:

Fine-tuned transformer models, especially BioBERT, are effective for multiclass clinical literature classification.
Hyperparameter optimization is critical for achieving robust model performance.
Future work should explore methods to address class imbalance for improved classification of all study types.