Multi‐institutional evaluation of a sinus surgery checklist
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To examine the frequency of safe surgical practices specific to endoscopic sinus surgery (ESS) before and after implementation of a checklist at four institutions across North America. STUDY DESIGN: Prospective, multi-institutional, observational study. METHODS: Consecutive surgeries were observed at four institutions before (n = 100) and after (n = 100) implementation of the ESS Checklist. A passive observer documented whether 10 specific tasks were performed by the surgical team during the course of each case. The frequency with which each item was performed was tabulated, and differences across institutions were evaluated using the Pearson χ(2) test. Improvement in the frequency of each single item between pre- and postintervention time periods was assessed by the McNemar χ(2) test. RESULTS: Successful performance of all 10 tasks in the prechecklist period was not observed for any ESS case at any of the four study sites. As might be expected, performance of any individual task was highly variable, ranging from 14% to 95%. After implementation of the ESS Checklist, successful performance of all 10 tasks during an individual surgery increased from 0% to 87% across all institutions, a change that was highly significant (P < .001). Significant increases in the performance of individual tasks was observed for nine of 10 items across all institutions (P ≤ .031 for all). CONCLUSIONS: Significant heterogeneity exists with regard to performance of specific tasks aimed at minimizing error during ESS. Utilization of the ESS Checklist standardized practice across four institutions and significantly increased the likelihood that individual safety tasks were performed during the course of sinus surgery.
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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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".