Cardiac surgery and the sustainable development goals: a review
Bibliographic record
Abstract
Abstract Background In 2015, the United Nations adopted the Sustainable Development Goals (SDGs) as key priorities to improve the global health and international development agenda in an intersectoral manner, highlighting 17 SDGs. Six billion people lack access to safe, timely, and affordable cardiac surgical care due to capacity, geographic, and financial barriers. Nevertheless, cardiac surgery is largely disregarded on the global health agenda. In this review, we explore the intersection between cardiac surgery and the SDGs to delineate potential policy and advocacy avenues for the cardiothoracic surgical community. Main body A narrative review was performed using the PubMed/MEDLINE, Scopus, and WHO databases with variations of the search terms “cardiac surgery,” “cardiovascular diseases,” and keywords extracted from individual SDGs. All SDGs were manually reviewed to define intersectionality with global cardiac surgery. Out of 17 SDGs, 15 are relevant and require additional attention from the cardiovascular community. SDG3, “Good Health and Well-being,” is the most relevant, although the intersection between global cardiac surgery and other SDGs is apparent. A call for interdisciplinary collaboration through increased preventive mechanisms, rigorous, all-inclusive clinical trials, advocacy with relevant legislators, and mobilizing capacity building mechanisms are made. Conclusion Meeting the SDGs will require recognition of cardiovascular disease management, including cardiac surgical care. Cardiac surgeons are essential stakeholders of multidisciplinary collaborations working to improve access to safe, timely, and affordable cardiac surgery for all. Their role as advocates will be vital to establish local, national, regional, and international partnerships and to ensure progress towards SDG attainment.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".