Parallels between neurologist training in Brazil and in other countries
Bibliographic record
Abstract
Introduction: Neurology training involves practice in infirmaries and outpatient clinics in several subspecialties, as well as training in procedures and examinations. The analysis of Medical Residency Programs (MRPs) in Neurology in other countries is important to identify points of contrast and similarities as a way to keep the national training equivalent to other countries. Objectives: To analyze the duration and characteristics of the training of neurology physicians in Brazil and other countries. Methods: Cross-sectional study by active search on official web pages of governments and organizations/entities representing neurologists from 12 countries: Australia, Portugal, Italy, Greece, India, USA, Canada, Puerto Rico, Argentina, Chile, Uruguay, and Colombia. Information was obtained on the duration of medical school and residency, as well as the characteristics of this. Results: The duration of medical school was 4 to 7 years (median: 6; IIQ: 0.5). Duration in neurology was 3 to 6 years (median: 4; IIQ:1). Developed countries have a median duration of residency of 4.83 years ± 0.68 years, whereas in developing countries it was 3.66 ±0.47 years. Regarding access, 25% of the countries require a prerequisite. Regarding rotations, those present in most of the programs studied were: neurology outpatient clinic (100%), neuroradiology (83%), neuropediatrics (75%) clinical medicine (58%), psychiatry (58%). Conclusion: We identified differences in the standardization of PRM in Neurology among the countries studied. The duration of Brazilian residency is below the average of the other countries studied, but it includes the required rotations in developed countries.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".