Building cardiac surgical programs in lower-middle income countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.000 | 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 | 0.001 |
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 it