Are LMICs Achieving the Lancet Commission Global Benchmark for Surgical Volumes? A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: The Lancet Commission on Global Surgery (LCoGS) set the benchmark of 5000 procedures per 100,000 population annually to meet surgical needs adequately. This systematic review provides an overview of the last ten years of surgical volumes in Low and Middle- Income-Countries (LMICs). METHODOLOGY: We searched PubMed, Web of Science, Scopus, Cochrane, and EMBASE databases for studies from LMICs addressing surgical volume. The number of surgeries performed per 100,000 population was estimated. We used cesarean sections, hernia, and laparotomies as index cases for the surgical capacities of the country. Their proportions to total surgical volumes were estimated. The association of country-specific surgical volumes and the proportion of index cases with its Gross Domestic Product (GDP) per capita was analyzed. RESULTS: A total of 26 articles were included in this review. In LMICs, on average, 877 surgeries were performed per 100,000 population. The proportion of cesarean sections was found to be high in all LMICs, with an average of 30.1% of the total surgeries, followed by hernia (16.4%) and laparotomy (5.1%). The overall surgical volumes increased as the GDP per capita increased. The proportions of cesarean section and hernia to total surgical volumes decreased with increased GDP per capita. Significant heterogeneity was found in the methodologies to assess surgical volumes, and inconsistent reporting hindered comparison between countries. CONCLUSION: Most LMICs have surgical volumes below the LCoGS benchmark of 5000 procedures per 100,000 population, with an average of 877 surgeries. The surgical volume increased while the proportions of hernia and cesarean sections reduced with increased GDP per capita. In the future, it's essential to apply uniform and reproducible data collection methods for obtaining multinational data that can be more accurately compared.
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How this classification was reachedexpand
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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.004 |
| Bibliometrics | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".