Cost‐Effectiveness in Global Surgery: Pearls, Pitfalls, and a Checklist
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
INTRODUCTION: Cost-effectiveness analysis can be a powerful policy-making tool. In the two decades since the first cost-effectiveness analyses in global surgery, the methodology has established the cost-effectiveness of many types of surgery in low- and middle-income countries (LMICs). However, with the crescendo of cost-effectiveness analyses in global surgery has come vast disparities in methodology, with only 15% of studies adhering to published guidelines. This has led to results that have varied up to 150-fold. METHODS: The theoretical basis, common pitfalls, and guidelines-based recommendations for cost-effectiveness analyses are reviewed, and a checklist to be used for cost-effectiveness analyses in global surgery is created. RESULTS: Common pitfalls in global surgery cost-effectiveness analyses fall into five categories: the analytic perspective, cost measurement, effectiveness measurement, probability estimation, valuation of the counterfactual, and heterogeneity and uncertainty. These are reviewed in turn, and a checklist to avoid these pitfalls is developed. CONCLUSION: Cost-effectiveness analyses, when done rigorously, can be very useful for the development of efficient surgical systems in LMICs. This review highlights the common pitfalls in these analyses and methods to avoid these pitfalls.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it