Evaluation of the impact of collaborative work by teams from the National Medical Residency Committee and the Brazilian Society of Neurosurgery. Retrospective and prospective study
Bibliographic record
Abstract
CONTEXT AND OBJECTIVE: Training for specialist physicians in Brazil can take place in different ways. Closer liaison between institutions providing this training and assessment and health care services may improve qualifications. This article analyzes the impact of closer links and joint work by teams from the National Medical Residency Committee (Comissão Nacional de Residência Médica, CNRM) and the Brazilian Society of Neurosurgery (Sociedade Brasileira de Neurocirurgia, SBN) towards evaluating these programs. DESIGN AND SETTING: Retrospective and prospective study, conducted in a public university on a pilot project developed between CNRM and SBN for joint assessment of training programs across Brazil. METHODS: The literature in the most relevant databases was reviewed. Documents and legislation produced by official government bodies were evaluated. Training locations were visited. Reports produced about residency programs were analyzed. RESULTS: Only 26% of the programs were immediately approved. The joint assessments found problems relating to teaching and to functioning of clinical service in 35% of the programs. The distribution of programs in this country has a strong relationship with the Human Development Index (HDI) of the regions and is very similar to the distribution of specialists. CONCLUSION: Closer collaboration between the SBN and CNRM had a positive impact on assessment of neurosurgery medical residency across the country. The low rates of direct approval have produced modifications and improvements to the quality of teaching and care (services). Closer links between the CNRM and other medical specialties have the capability to positively change the structure and function of specialty training in Brazil.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".