Surgical site infection prevention: a survey to identify the gap between evidence and practice in University of Toronto teaching hospitals
Bibliographic record
Abstract
BACKGROUND: A gap exists between the best evidence and practice with regards to surgical site infection (SSI) prevention. Awareness of evidence is the first step in knowledge translation. METHODS: A web-based survey was distributed to 59 general surgeons and 68 residents at University of Toronto teaching hospitals. Five domains pertaining to SSI prevention with questions addressing knowledge of prevention strategies, efficacy of antibiotics, strategies for changing practice and barriers to implementation of SSI prevention strategies were investigated. RESULTS: Seventy-six individuals (60%) responded. More than 90% of respondents stated there was evidence for antibiotic prophylaxis and perioperative normothermia and reported use of these strategies. There was a discrepancy in the perceived evidence for and the self-reported use of perioperative hyperoxia, omission of hair removal and bowel preparation. Eighty-three percent of respondents felt that consulting published guidelines is important in making decisions regarding antibiotics. There was also a discrepancy between what respondents felt were important strategies to ensure timely administration of antibiotics and what strategies were in place. Checklists, standardized orders, protocols and formal surveillance programs were rated most highly by 75%-90% of respondents, but less than 50% stated that these strategies were in place at their institutions. CONCLUSION: Broad-reaching initiatives that increase surgeon and trainee awareness and implementation of multifaceted hospital strategies that engage residents and attending surgeons are needed to change practice.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".