Systematic review of the Lancet Commission on Global Surgery indicators with quality assessment of modelled estimates
Bibliographic record
Abstract
BACKGROUND: The Lancet Commission on Global Surgery (LCoGS) defined six indicators with 2030 targets to track national surgical system performance. The aim of this systematic review was to evaluate national reporting and attainment of benchmarks for each indicator and to assess the quality of modelling studies used to fill data gaps. METHODS: Seven bibliographic databases (1 April 2015-24 July 2024) and government domains of 48 countries committed to National Surgical, Obstetric, and Anaesthesia Plans were searched. Records providing national estimates of any LCoGS indicator were eligible. The primary outcome was the proportion of World Bank-classified countries meeting indicator benchmarks and the secondary outcome was the quality of modelled national estimates. This systematic review was prospectively registered in PROSPERO, the international prospective register of systematic reviews (CRD420250650890). RESULTS: Of 4245 records retrieved, 44 studies were included (35 research articles and 9 policy documents). Among 217 World Bank-classified countries, access to timely essential surgery (indicator 1) was reported for 94 countries (39% meeting benchmark), specialist surgical workforce density (indicator 2) was reported for 167 countries (50.3% meeting benchmark), surgical volume (indicator 3) was reported for 124 countries (31.5% meeting benchmark), perioperative mortality (indicator 4) was reported for 74 countries (no benchmark was set at country level), and financial risk protection indicators (indicators 5 and 6) were reported for five countries, with none meeting either benchmark. Across indicators, high-income countries were more likely to meet benchmarks. Most modelled studies lacked transparency in data sources, statistical methods, or model validation. CONCLUSION: Reporting of LCoGS indicators remains sparse and uneven, particularly in low- and middle-income countries. Without standardized, routine measurement and minimum quality standards for modelled estimates, progress towards 2030 cannot be credibly tracked. Integrating surgical metrics into national health information systems should be a policy priority.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".