Evaluation of a preoperative team briefing: a new communication routine results in improved clinical practice
Bibliographic record
Abstract
BACKGROUND Suboptimal communication within healthcare teams can lead to adverse patient outcomes. Team briefings were previously associated with improved communication patterns, and we assessed the impact of briefings on clinical practice. To quantify the impact of the preoperative team briefing on direct patient care, we studied the timing of preoperative antibiotic administration as compared to accepted treatment guidelines. STUDY DESIGN A retrospective pre-intervention/post-intervention study design assessed the impact of a checklist-guided preoperative team briefing on prophylactic antibiotic administration timing in surgical cases (N=340 pre-intervention and N=340 post-intervention) across three institutions. χ(2) Analyses were performed to determine whether there was a significant difference in timely antibiotic administration between the study phases. RESULTS The process of collecting and analysing these data proved to be more complicated than expected due to great variability in documentation practices, both between study sites and between individual practitioners. In cases where the timing of antibiotics administration was documented unambiguously in the chart (n=259 pre-intervention and n=283 post-intervention), antibiotic prophylaxis was on time for 77.6% of cases in the pre-intervention phase of the study, and for 87.6% of cases in the post-intervention phase (p<0.01). CONCLUSIONS Use of a preoperative team checklist briefing was associated with improved physician compliance with antibiotic administration guidelines. Based on the results, recommendations to enhance timely antibiotic therapy are provided.
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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 | 0.014 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".