Building cardiac surgical programs in lower-middle income countries
Bibliographic record
Abstract
Objectives: Medical care in low-income countries is often limited by inadequate resources, treatment facilities, and the necessary infrastructure for healthcare delivery. We hypothesized that the development of an independently functioning, internationally supported Kenyan cardiac surgical training program could address these issues through targeted investment. Methods: A review was conducted of the programmatic structure and clinical outcomes from January 2008 to October 2021 at Tenwek Hospital, Bomet, Kenya. Program development phases included (1) cardiovascular care provided by 1 full-time US board-certified cardiothoracic surgeon; (2) short-term volunteer surgical teams from the United States and Canada; and (3) development of a cardiothoracic residency program based on the Society of Thoracic Surgeons training curriculum. Patient demographics and outcomes were analyzed throughout each phase of program development. Results: A total of 817 cardiac procedures were performed during the study period, including 236 congenital (28.8%) and 581 adult (71.1%) procedures. Endemic rheumatic valvular heart disease predominated (581 patients, 62.3%). Local surgical team case volume grew over the study period, overtaking visiting team volume in 2019. Perioperative mortality was 2.1% and consistent between the visiting teams and the locally trained teams. Surgical training via a 3-year cardiothoracic residency is now in its fourth year, with the 2 graduates now retained as full-time teaching staff. Conclusions: Global health partnerships have the potential to address unmet needs in cardiac care within low- and middle-income countries. These data support the concept that acceptable clinical outcomes and consistent growth in volume can be achieved during the transition toward fully independent cardiac surgical care.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".