Improving Antibiotic Selection
Bibliographic record
Abstract
OBJECTIVE: We sought to assess which interventions are most effective at improving the prescribing of recommended antibiotics for acute outpatient infections. DESIGN AND METHODS: We undertook a systematic review with quantitative analysis of the Cochrane Registry Effective Practice and Organization of Care (EPOC) database, supplemented by MEDLINE and hand-searches. Inclusion criteria included clinical trials with contemporaneous or strict historical controls that reported data on antibiotic selection in acute outpatient infections. The effect size of studies with different intervention types were compared using nonparametric statistics. To maximize comparability between studies, quantitative analysis was restricted to studies that reported absolute changes in the amount of or percent compliance with recommended antibiotic prescribing. RESULTS: Twenty-six studies reporting 33 trials met inclusion criteria. Most interventions used clinician education alone or in combination with audit and feedback. Among the 22 comparisons amenable to quantitative analysis, recommended antibiotic prescribing improved by a median of 10.6% (interquartile range [IQR] 3.4-18.2%). Trials evaluating clinician education alone reported larger effects than interventions combining clinician education with audit and feedback (median effect size 13.9% [IQR 8.6-21.6%] vs. 3.4% [IQR 1.8-9.7%], P = 0.03). This result was confounded by trial sample size, as trials having a smaller number of participating clinicians reported larger effects and were more likely to use clinician education alone. Active forms of education, sustained interventions, and other features traditionally associated with successful quality improvement interventions were not associated with effect size and showed no evidence of confounding the association between clinician education-only strategies and outcome. CONCLUSIONS: Multidimensional interventions using audit and feedback to improve antibiotic selection were less effective than interventions using clinician education alone. Although confounding may partially account for this finding, our results suggest that enhancing the intensity of a focused intervention may be preferable to a less intense, multidimensional approach.
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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 | 0.017 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".