From definitions to implementation – A guide to collect and apply the Lancet Commission on Global Surgery indicators: An Utstein consensus report
Bibliographic record
Abstract
ABSTRACT Metrics for surgery, obstetrics, and anesthesia are crucial for implementing programs and monitoring progress toward safe and effective healthcare systems in pursuit of universal healthcare. A suite of metrics put forward by the Lancet Commission on Global Surgery has been adopted in principle by global health, anaesthesia, and surgical leadership in diverse settings. However, barriers to implementation limit their value. Barriers include inconsistencies in definitions and methodologies such as inadequate consideration given to sampling frames, representativeness, categorizations, missing data, and data collection infrastructure. Using the Utstein consensus methodology, we developed a uniform approach to collecting metrics in surgery, obstetrics, and anesthesia. We created a standard toolkit to facilitate the rapid implementation of the Lancet Commission indicators. The metadata and data dictionaries allow a standardized assessment of preparedness for, delivery of, and the effect of care at the population level.
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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 | 0.434 | 0.384 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.026 | 0.023 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.016 | 0.019 |
| Research integrity | 0.008 | 0.020 |
| Insufficient payload (model declined to judge) | 0.010 | 0.012 |
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".