Outcomes for patients with rheumatic heart disease after cardiac surgery followed at rural district hospitals in Rwanda
Bibliographic record
Abstract
BACKGROUND: In sub-Saharan Africa, continued clinical follow-up, after cardiac surgery, is only available at urban referral centres. We implemented a decentralised, integrated care model to provide longitudinal care for patients with advanced rheumatic heart disease (RHD) at district hospitals in rural Rwanda before and after heart surgery. METHODS: We collected data from charts at non-communicable disease (NCD) clinics at three rural district hospitals in Rwanda to describe the outcomes of 54 patients with RHD who received cardiac valve surgery during 2007-2015. RESULTS: The majority of patients were adults (46/54; 85%), and 74% were females. The median age at the time of surgery was 22 years in adults and 11 years in children. Advanced symptoms-New York Heart Association class III or IV-were present in 83% before surgery and only 4% afterwards. The mitral valve was the most common valve requiring surgery. Valvular surgery consisted mostly of a single valve (56%) and double valve (41%). Patients were followed for a median of 3 years (range 0.2-7.9) during which 7.4% of them died; all deaths were patients who had undergone bioprosthetic valve replacement. For patients with mechanical valves, anticoagulation was checked at 96% of visits. There were no known bleeding or thrombotic events requiring hospitalisation. CONCLUSION: Outcomes of postoperative patients with RHD tracked in rural Rwanda health facilities were generally good. With appropriate training and supervision, it is feasible to safely decentralise follow-up of patients with RHD to nurse-led specialised NCD clinics after cardiac surgery.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".