Evaluating the collection, comparability and findings of six global surgery indicators
Bibliographic record
Abstract
BACKGROUND: In 2015, six indicators were proposed to evaluate global progress towards access to safe, affordable and timely surgical and anaesthesia care. Although some have been adopted as core global health indicators, none has been evaluated systematically. The aims of this study were to assess the availability, comparability and utility of the indicators, and to present available data and updated estimates. METHODS: Nationally representative data were compiled for all World Health Organization (WHO) member states from 2010 to 2016 through contacts with official bodies and review of the published and grey literature, and available databases. Availability, comparability and utility were assessed for each indicator: access to timely essential surgery, specialist surgical workforce density, surgical volume, perioperative mortality, and protection against impoverishing and catastrophic expenditure. Where feasible, imputation models were developed to generate global estimates. RESULTS: Of all WHO member states, 19 had data on the proportion of the population within 2h of a surgical facility, 154 had data on workforce density, 72 reported number of procedures, and nine had perioperative mortality data, but none could report data on catastrophic or impoverishing expenditure. Comparability and utility were variable, and largely dependent on different definitions used. There were sufficient data to estimate that worldwide, in 2015, there were 2 038 947 (i.q.r. 1 884 916-2 281 776) surgeons, obstetricians and anaesthetists, and 266·1 (95 per cent c.i. 220·1 to 344·4) million operations performed. CONCLUSION: Surgical and anaesthesia indicators are increasingly being adopted by the global health community, but data availability remains low. Comparability and utility for all indicators require further resolution.
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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 | 0.291 | 0.421 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.022 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".