Global Access to Cardiac Surgery Centers: Distribution, Disparities, and Targets
Bibliographic record
Abstract
BACKGROUND: Global data on cardiac surgery centers are outdated and survey-based. In 1995, there were 0.7 centers per million population, ranging from one per 120,000 in North America to one per 33 million in sub-Saharan Africa. This study analyzes the contemporary distribution of cardiac surgery centers and proposes targets relative to countries' cardiovascular disease (CVD) burdens. METHODS: Medical databases, gray literature, and governmental reports were used to identify the most recent post-2010 data that describe the number of centers performing cardiac surgery in each nation. The 2019 Institute for Health Metrics and Evaluation Global Burden of Disease Results Tool provided national CVD burdens. One-third of the CVD burden was assumed to be surgical. Center targets were proposed as the average or half of the average of centers per million surgical CVD patients in high-income countries. RESULTS: 5,111 cardiac surgery centers were identified across 230 nations and territories with available data, equaling 0.73 centers per million population. The median (interquartile range) number of centers ranged from 0 (0-0.06) per million in low-income countries to 0.75 (0-1.44) in high-income countries. Targets were 612.2 (optimistic) or 306.1 (conservative) centers per million surgical CVD incidence. In 2019, low-income, lower-middle-income, and upper-middle-income countries possessed 34.8, 149.0, and 271.9 centers per million surgical CVD incidence. CONCLUSION: Little progress has been made to increase cardiac surgery centers per population despite growing CVD burdens. Today's global cardiac surgical capacity remains insufficient, disproportionately affecting the world's poorest regions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".