General Thoracic Surgery in Rwanda: An Assessment of Surgical Volume and of Workforce and Material Resource Deficits
Bibliographic record
Abstract
BACKGROUND: Benchmarking operative volume and resources is necessary to understand current efforts addressing thoracic surgical need. Our objective was to examine the impact on thoracic surgery volume and patient access in Rwanda following a comprehensive capacity building program, the Human Resources for Health (HRH) Program, and thoracic simulation training. METHODS: A retrospective cohort study was conducted of operating room registries between 2011 and 2016 at three Rwandan referral centers: University Teaching Hospital of Kigali, University Teaching Hospital of Butare, and King Faisal Hospital. A facility-based needs assessment of essential surgical and thoracic resources was performed concurrently using modified World Health Organization forms. Baseline patient characteristics at each site were compared using a Pearson Chi-squared test or Kruskal-Wallis test. Comparisons of operative volume were performed using paired parametric statistical methods. RESULTS: Of 14,130 observed general surgery procedures, 248 (1.76%) major thoracic cases were identified. The most common indications were infection (45.9%), anatomic abnormalities (34.4%), masses (13.7%), and trauma (6%). The proportion of thoracic cases did not increase during the HRH program (2.07 vs 1.78%, respectively, p = 0.22) or following thoracic simulation training (1.95 2013 vs 1.44% 2015; p = 0.15). Both university hospitals suffer from inadequate thoracic surgery supplies and essential anesthetic equipment. The private hospital performed the highest percentage of major thoracic procedures consistent with greater workforce and thoracic-specific material resources (0.89% CHUK, 0.67% CHUB, and 5.42% KFH; p < 0.01). CONCLUSIONS AND RELEVANCE: Lack of specialist providers and material resources limits thoracic surgical volume in Rwanda despite current interventions. A targeted approach addressing barriers described is necessary for sustainable progress in thoracic 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".