Insights Into the Assessment of WHO-Recommended Practices Based on Surgical Operations in Tertiary Healthcare Settings
Bibliographic record
Abstract
BACKGROUND: The protection of surgical safety comprises two major elements that produce the best outcomes for patients. The World Health Organization (WHO) created a surgical practice-based assessment for operative procedure-related risks. Medical professionals have proven the effectiveness of this checklist to eliminate both adverse outcomes and medical complications caused by surgical negligence. METHODOLOGY: A study was conducted on 250 surgeries (major and minor) in a tertiary healthcare setting based on a qualitative questionnaire, adapted from the WHO checklist, operated through Google Forms. The examination spanned three months from September to November 2024. In accordance with WHO guidelines, the three surgical safety checklist phases - sign-in, time-out, and sign-out - were analyzed using SPSS version 20.0 (IBM Corp., Armonk, NY). RESULTS: The sign-out phase achieved the highest level of adherence, with 220 (88%) of surgical procedures using the checklist. The sign-in phase demonstrated 200 compliant cases (80%), whereas the time-out phase showed the lowest compliance, with only 170 cases (68%). Patient consent procedures, along with anesthesia protocols, instrument sterilization methods, and team member introduction protocols, all maintained complete success rates for ensuring a safe surgical space. CONCLUSIONS: Implementing targeted awareness programs and training will help boost compliance rates with the WHO checklist, despite the current positive results.
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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 | 0.022 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".