Cardiology Training in Brazil and Developed Countries: Some Ideas for Improvement
Bibliographic record
Abstract
Huge variations exist in cardiology training programs across the world. In developing (middle-income) countries, such as Brazil, to find the right balance between training improvements and social and economic conditions of the country may be a difficult task. Adding more training years or different mandatory rotations, for instance, may be costly and not have an immediate direct impact on enhancing patient care or public health. In this text, we compare the Brazilian cardiology training system with other proposals implemented in developed countries from North America and Europe, aiming to point out issues worth of future discussion. Factors such as training rotations and competencies, and program duration and distribution across the countries are presented. The number of first year cardiology trainees per inhabitants is similar between Brazil and the United States (0.24 medical residents/100,000 inhabitants in Brazil and 0.26 medical residents/100,000 inhabitants in the USA). These numbers should be analyzed considering the inequality in training program distribution across Brazil, since most centers are located in the Southeast and South regions. Having more residency programs in distant areas could improve cardiovascular care in these areas. Duration of cardiology Residency Training is shorter in Brazil (two years) in comparison with developed countries (> 3 years). Brazilian residency programs give less emphasis to scientific research and diagnostic methods. Unifying minimum training requirements across the globe would facilitate the development of international learning opportunities and even professional exchange around the world.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".