Características de la formación de los cardiólogos en América Latina: una encuesta de la Sociedad Interamericana de Cardiología
Bibliographic record
Abstract
Objectives: Describe the characteristics of the different cardiology medical residencies in Latin America. Method: Cross-sectional study that aims to evaluate the characteristics of cardiology residencies in Spanish-speaking countries of Latin America, through self-administered electronic surveys. Results: Three hundred seven residents of 147 residences were surveyed. Mean age was 31 years and 63% were male. Ninety eight percent carry out their training in the capital city. The average total training time is 4.8 years. Forty four percent complete their residency in internal medicine prior to starting cardiology, and 10% have no prior training. In cardiology training is 3 years in most countries. Fifty four percent present academic activities every day and 16% only once or less, consisting of theoretical classes (93%), clinical cases (85%), bibliographic workshops (69%), and writing scientific papers (68%). Supervision is carried out by the chief resident (45%), resident coordinator (44%), resident instructor (27%) or the department head (54%), while 2.6% do not present supervision. The main rotations were echocardiography (99%), hemodynamics (96%), coronary unit (93%), and electrophysiology (92%). Residents highlighted the need to improve academic activities (23%) and scientific production (12%). Conclusions: There are important differences in the academic and practical training between the residences of the different countries of America.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".