The Current State and Challenges in Cardiothoracic Surgery in Latin America
Bibliographic record
Abstract
America's cardiothoracic landscape is marked by stark contrasts.While highvolume centers achieve outcomes comparable to high-income nations, systemic inequities persist.The region faces a critical workforce shortage (<1 surgeon per million population), with training programs concentrated in larger urban hubs like Sao Paulo, Buenos Aires, and Mexico City, leaving rural areas underserved. 1 Access to advanced therapies such as transcatheter aortic valve replacement and left ventricular assist devices remains limited by funding disparities-Peru's transplant programs, for example, operate at 59% capacity due to reliance on philanthropic support. 2 Gender gaps further compound these challenges, with women representing only 4.8% of surgeons.The Latin American Association of Cardiac and Endovascular Surgery (LACES) has unified regional education efforts, but broader reforms are needed to standardize training, expand infrastructure, and leverage telemedicine for equitable care delivery.Without addressing these foundational gaps, technological advancements risk benefiting only a privileged few.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".