Internal Audit of Compliance with a Perioperative Checklist in a Tertiary Care Neurosurgical Unit
Bibliographic record
Abstract
BACKGROUND: In 1999, the Institute of Medicine reported that, in the United States, 44,000 to 98,000 people die annually as a result of avoidable medical errors. Among the many initiatives undertaken to stem avoidable surgical errors, the World Health Organization (WHO) Surgical Safety Checklist has certainly been one of the most successful. Many surgical units have implemented adapted versions of the WHO Surgical Safety Checklist, audited their performance and discussed issues relating to the implementation process. However, such literature is still lacking in neurosurgery. METHODS: A prospective observational study of 171 neurosurgical cases was conducted over an 8-week period. An independent observer assessed compliance with and completeness of the three steps in the perioperative checklist: Sign-in, Time-out and Sign-out. Factors that may reduce compliance were also analyzed. RESULTS: Compliance with the Sign-in, Time-out and Sign-out steps was 82%, 99% and 93% respectively. On average, 92% of the Time-out elements were verified. The emergent nature of a surgery was the only factor that caused a statistically significant reduction in compliance with the checklist. Overall compliance diminished during the observation period. CONCLUSION: In this internal audit study, compliance with the preoperative checklist reached a satisfactory level. Further work is still needed, however, on some aspects of our surgical strategy, namely, a relatively low compliance rate with the Sign-in process was recorded and emergent cases were associated with decreased performance.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".