Auto‐Substitutions to Optimize Perioperative Antimicrobial Prophylaxis: Pre‐Post Intervention Study
Bibliographic record
Abstract
BACKGROUND: Appropriate administration of perioperative antibiotics can prevent antimicrobial resistance, adverse drug events, surgical site infections, and increased costs to the health care system for many surgeries in Otolaryngology-Head and Neck Surgery (OHNS). OBJECTIVE: The objective of the study is to achieve 90% compliance with evidence-based perioperative antibiotic prophylaxis guidelines among elective surgical procedures in OHNS. METHODS: The pre-intervention group consisted of patients undergoing elective surgical procedures in the 13 months prior to the interventions (September 2019-2020) whereas the post-intervention group comprised patients undergoing elective procedures during the 8 months following the implementation (October 2020-May 2021). The 4 Es of knowledge translation and the Donabedian framework were used to frame the study. Components of the intervention included educational grand rounds and automatic substitutions in electronic health records. In June 2021, a survey of staff and residents assessed the self-reported perception of following evidence-based guidelines. RESULTS: Compliance with antimicrobial prophylaxis guidelines were evaluated based on agent and dose. The overall compliance improved from 38.8% pre-intervention to 59.0% post-intervention (p < 0.001). Agent compliance did not improve from pre- to post-intervention, that is, 60.7% to 62.8%, respectively, (p = 0.68), whereas dose compliance improved from 39.6% to 89.2% (p < 0.001). Approximately 78.5% of survey respondents felt that they strongly agreed or agreed with always following evidence-based antimicrobial prophylaxis guidelines. CONCLUSION: Compliance with antimicrobial prophylaxis guidelines improved, primarily due to increased dosing compliance. Future interventions will target agent compliance and selected procedures with lower compliance rates. LEVEL OF EVIDENCE: 3 Laryngoscope, 133:3403-3408, 2023.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".