The state of cardiac surgery in Ethiopia
Bibliographic record
Abstract
Objectives: Six billion people globally do not have access to cardiac surgical care. In this study, we aimed to describe state of cardiac surgery in Ethiopia. Methods: Data on status of local cardiac surgery collected from surgeons and cardiac centers. Medical travel agents were interviewed about number of cardiac patients who were assisted to travel abroad for surgery. Historical data and number of patients treated by non-governmental organizations were collected via interviews and by accessing existing databases. Results: Patients access cardiac care via 3 avenues: mission-based, abroad referral, and care at local centers. Traditionally, the first 2 have been the main mode of access; however, since 2017, an entirely local team has begun performing heart surgery in the country. Currently, surgical cardiac care is provided at 4 local centers: a charity organization, a tertiary public hospital, and 2 for-profit centers. Procedures at the charity center are provided for free, whereas in others, patients mostly pay out of pocket. There are only 5 cardiac surgeons for 120 million people. More than 15,000 patients are on waitlist for surgery, mainly because of lack of consumables and limited numbers of centers and workforce. Conclusions: There is a change in the trend from non-governmental mission- and referral-based care toward care in local centers in Ethiopia. The local cardiac surgery workforce is growing but still insufficient. The number of procedures is limited with long wait lists due to limited workforce, infrastructure, and resources. All stakeholders should work on training more workforce, providing consumables, and creating feasible financing schemes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".