Global Cardiac Surgical Volume and Gaps: Trends, Targets, and Way Forward
Bibliographic record
Abstract
Background: More than 1 million cardiac surgical procedures are estimated to occur yearly. Little is known about country-level procedural estimates and volumes needed to provide adequate population coverage. We evaluated annual cardiac procedural volumes for high-income countries and present target volumes for countries to work toward as part of universal health coverage agendas. Methods: Academic and gray literature was searched for total annual volumes for high-income countries for all cardiac surgery, coronary artery bypass grafting (CABG), valvular surgery, and congenital heart surgery between 2010 and 2021. Matched populations were obtained from the World Bank World Development Indicators. Volume targets by country income group were proposed on the basis of published expert opinion and adjusted for cardiovascular disease burdens. Results: An average total cardiac surgical volume of 123.2 per 100,000 population per year was performed in high-income countries (36.7 CABG, 30.8 valvular, 7.9 congenital). Unadjusted annual volume targets per 100,000 population for low- and middle-income countries are 61.6 cardiac surgical procedures, 18.3 CABGs, 15.4 valvular surgical procedures, and 4.0 congenital heart operations. Adjusted for cardiovascular disease burdens, total cardiac surgical volume targets are 86.1 procedures per 100,000 population per year for upper-middle-income countries, 55.1 for lower-middle-income countries, and 40.2 for low-income countries. Conclusions: Target annual procedural volumes present opportunities to strategically work toward expanding cardiac surgical capacity to meet the needs of countries' populations. These targets may guide country-specific targets, which should be optimized through the expert opinion and lived experiences of local health care professionals and the context-specific population needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".