Comprehensive portrait of stroke fellowship training in Brazil: A national survey study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The field of vascular neurology has undergone significant advances over the last decade, and care has become more complex. However, vascular neurology training programs remain underdeveloped in many countries, despite stroke impact on health care. There are efforts towards building a nationally regulated curricula in some countries. Still, comprehensive planning and implementation of these programs may be needed on a global scale, especially in countries where stroke treatment is not fully implemented. We aim to comprehensively analyze vascular neurology trainees' profiles in Brazil to describe training program contents from trainees and program directors' perspectives. METHODS: We performed an observational, cross-sectional, web-based survey study to describe trainee and program-specific characteristics at vascular neurology fellowship training programs in Brazil. The study was conducted from June to September 2023 using a secure web-based survey sent to active fellows and program directors from all known vascular neurology fellowship programs in the country. All respondents were required to provide informed consent. RESULTS: We obtained a 100 % response rate of a total of 12 programs distributed in 7 federal states. Notably, 57 % of the 28 surveyed fellows were women, 60 % were aged 25-30, and 70 % self-identified as White. All fellows had prior neurology training, and 60 % engaged in the program just following residency. Exposure to various training experiences was favorable, except for simulation-based learning and telestroke training. Program directors perceived exposure to be sufficient for most components but similarly found deficiencies in telestroke and simulation-based learning. Scientific productivity was low, with about two-thirds of fellows having no publications or abstracts. Most fellows (92.6 %) reported performing non-fellowship medical activities to supplement their incomes. DISCUSSION: In conclusion, the number of vascular neurology training programs and trainees in Brazil is currently insufficient and exhibits an uneven geographic distribution. Despite this, the clinical training provided is extensive, and there is generally some funding available for fellows. These insights highlight the need for strategic improvements in Brazil's stroke education and could inform similar developments in other nations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".