Dimensions overall score 7.0

• The mmBERT-base model achieved the best performance in clinical named entity recognition for Portuguese, with a micro F1-score of 0.76.result 95%
• Iterative stratification was an effective strategy for improving class balance and overall performance in clinical NER.method 90%
• Multilingual BERT models, particularly mmBERT, perform strongly for Portuguese clinical NER and can run locally with limited computational resources.technical 90%
• BERT models generally demonstrated better performance compared to leading LLMs for Portuguese clinical NER.result 85%
• The study compared BioBERTpt, BERTimbau, ModernBERT, and mmBERT with LLMs such as GPT-5 and Gemini-2.5.method 95%
• The mmBERT-base model established a new state-of-the-art on the SemClinBr dataset with a micro F1-score of 0.7646.result 95%
• LLMs, when prompted with increased reasoning effort, showed a modest performance gain but did not surpass every BERT model.result 85%
• The mmBERT-base model achieved the best performance in clinical named entity recognition for Portuguese, with a micro F1-score of 0.76.result 95%
• Iterative stratification is an effective strategy for improving class balance and overall performance in clinical NER tasks.method 90%
• Multilingual BERT models, particularly mmBERT, perform strongly for Portuguese clinical NER and can be run locally with limited computational resources.technical 95%
• BERT models generally demonstrate better performance compared to leading LLMs for Portuguese clinical NER, with faster inference and local execution capabilities.result 90%
• The study evaluated BioBERTpt, BERTimbau, ModernBERT, and mmBERT models against LLMs such as GPT-5 and Gemini-2.5 for clinical NER in Portuguese.method 100%

Competitive landscape

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