Variation in global uptake of the Surgical Safety Checklist
Bibliographic record
Abstract
BACKGROUND: The Surgical Safety Checklist (SSC) is a patient safety tool shown to reduce mortality and to improve teamwork and adherence with perioperative safety practices. The results of the original pilot work were published 10 years ago. This study aimed to determine the contemporary prevalence and predictors of SSC use globally. METHODS: Pooled data from the GlobalSurg and Surgical Outcomes studies were analysed to describe SSC use in 2014-2016. The primary exposure was the Human Development Index (HDI) of the reporting country, and the primary outcome was reported SSC use. A generalized estimating equation, clustering by facility, was used to determine differences in SSC use by patient, facility and national characteristics. RESULTS: A total of 85 957 patients from 1464 facilities in 94 countries were included. On average, facilities used the SSC in 75·4 per cent of operations. Compared with very high HDI, SSC use was less in low HDI countries (odds ratio (OR) 0·08, 95 per cent c.i. 0·05 to 0·12). The SSC was used less in urgent compared with elective operations in low HDI countries (OR 0·68, 0·53 to 0·86), but used equally for urgent and elective operations in very high HDI countries (OR 0·96, 0·87 to 1·06). SSC use was lower for obstetrics and gynaecology versus abdominal surgery (OR 0·91, 0·85 to 0·98) and where the common or official language was not one of the WHO official languages (OR 0·30, 0·23 to 0·39). CONCLUSION: Worldwide, SSC use is generally high, but significant variability exists. Implementation and dissemination strategies must be developed to address this variability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".