Fine-Tuning Pre-trained Transformer-Based Models for Sentence-Level Medical Text Classification
Bibliographic record
Abstract
This paper presents the development of sentence-level medical text classifiers by fine-tuning eight pre-trained transformer-based models on the PubMed 20k RCT dataset. The models span both general-purpose and biomedical-specific architectures. To enhance performance and address class imbalance, a composite loss function combining cross-entropy, focal loss, and dice loss was applied during training. The fine-tuned models were trained on PubMed 20k RCT and then applied, without further adaptation, to the MTSamples dataset using balanced and imbalanced test subsets. ClinicalBERT achieved the highest results, reaching 97.15% accuracy and 96.93% F1-score on PubMed 20k RCT, 95.20% accuracy and 95.10% F1-score on the balanced MTSamples subset, and 91.80% accuracy and 90.60% F1-score on the imbalanced subset, indicating strong transferability across structured and unstructured medical texts. These outcomes highlight the effectiveness of domain-specific fine-tuning combined with optimized training strategies in building accurate and adaptable medical sentence classifiers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".