Clinical named entity recognition in the Portuguese language: a benchmark of modern BERT models and LLMs

Clinical named entity recognition in the Portuguese language: a benchmark of modern BERT models and LLMs | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/clinical-named-entity-recognition-in-the-portuguese-language-a-benchmark-of-modern-bert-models-and-llms

stale

Proof freshness: stale
Proof status: verified
Display score: 7/10
Last proof check: 2026-03-30
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 12
Source count: 5
Coverage: 83%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID clinical-named-entity-recognition-in-the-portuguese-language-a-benchmark-of-modern-bert-models-and-llms | Route /signal-canvas/clinical-named-entity-recognition-in-the-portuguese-language-a-benchmark-of-modern-bert-models-and-llms

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/clinical-named-entity-recognition-in-the-portuguese-language-a-benchmark-of-modern-bert-models-and-llms

MCP example

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source_context

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Evidence Receipt

Route status: stale

Claims: 12

References: 12

Proof: Verified

Freshness state: stale

Source paper: Clinical named entity recognition in the Portuguese language: a benchmark of modern BERT models and LLMs

PDF: https://arxiv.org/pdf/2603.26510v1

Repository: https://github.com/GRUPOMED4U/clinical_ner_benchmark_paper

Source count: 5

Coverage: 83%

Last proof check: 2026-03-30T20:30:32.666Z

Signal Canvas receipt window

Ready for execution: Clinical named entity recognition in the Portuguese language: a benchmark of modern BERT models and LLMs

/buildability/clinical-named-entity-recognition-in-the-portuguese-language-a-benchmark-of-modern-bert-models-and-llms

Build Nowready

Subject: Clinical named entity recognition in the Portuguese language: a benchmark of modern BERT models and LLMs

Verdict

GitHub Code Pulse

Cached

Stars

Health

Last commit

3/26/2026

Forks

Open repository

Claim map

Strong 12Mixed 0Weak 0

Evidencepartial
The mmBERT-base model achieved the best performance (micro F1 = 0.76), outperforming all other models.
Implicationpartial
This is a direct result stated in the abstract and confirmed in the results section with a specific F1 score.
Verificationpartial
partial
Evidencepartial
Iterative stratification improved class balance and overall performance.
Implicationpartial
The abstract explicitly states this as a finding, and the results section implies its positive impact.
Verificationpartial
partial
Evidencepartial
Multilingual BERT models, particularly mmBERT, perform strongly for Portuguese clinical NER and can run locally with limited computational resources.
Implicationpartial
This claim is directly stated in the abstract and supported by the results showing mmBERT's superior performance and the technical advantage of local execution.
Verificationpartial
partial
Evidencepartial
BERT models, in general, have demonstrated better performance when compared to leading LLMs in the industry.
Implicationpartial
The results section states this comparison, highlighting BERT's advantage over LLMs in this specific task.
Verificationpartial
partial
Evidencepartial
We compared BioBERTpt, BERTimbau, ModernBERT, and mmBERT with LLMs such as GPT-5 and Gemini-2.5
Implicationpartial
This is a direct statement of the models included in the comparison, found in both the abstract and methods section.
Verificationpartial
partial
Evidencepartial
mmBERT-base established a new state-of-the-art on the SemClinBr dataset (micro F1-score = 0.7646).
Implicationpartial
This is a specific and verifiable result with a precise F1 score, directly stated in the results section.
Verificationpartial
partial
Evidencepartial
The experiments involving increased reasoning effort using the gpt-5 model demonstrate a modest gain in performance that was not able to surpass every BERT model.
Implicationpartial
The results section discusses the impact of increased reasoning effort on LLMs, noting the limited performance improvement.
Verificationpartial
partial
Evidencepartial
The mmBERT-base model achieved the best performance (micro F1 = 0.76), outperforming all other models.
Implicationpartial
The abstract and results section explicitly state this finding and provide the specific F1-score.
Verificationpartial
partial
Evidencepartial
Iterative stratification improved class balance and overall performance.
Implicationpartial
The abstract and results section mention the exploration and positive impact of iterative stratification.
Verificationpartial
partial
Evidencepartial
Multilingual BERT models, particularly mmBERT, perform strongly for Portuguese clinical NER and can run locally with limited computational resources.
Implicationpartial
This is a direct conclusion stated in the abstract, highlighting both performance and technical feasibility.
Verificationpartial
partial
Evidencepartial
BERT models, in general, have demonstrated better performance when compared to leading LLMs in the industry. They also had faster inference and ran locally on domestic hardware, enabling experiments with no privacy risks.
Implicationpartial
The results section directly compares BERT models to LLMs and highlights these advantages.
Verificationpartial
partial
Evidencepartial
We compared BioBERTpt, BERTimbau, ModernBERT, and mmBERT with LLMs such as GPT-5 and Gemini-2.5, using the public SemClinBr corpus and a private breast-cancer dataset.
Implicationpartial
This is a direct statement of the models compared in the abstract and methods section.
Verificationpartial
partial

Competitive landscape

No named competitor graph is public yet; the page still exposes the segment, adoption evidence, and score state so the commercial read is not blank.

Segment

Research market

Adoption evidence

No public code link in the paper record yet

Commercial read

score refresh pending

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Clinical named entity recognition in the Portuguese language: a benchmark of modern BERT models and LLMs

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