Audit and Feedback Interventions for Antibiotic Prescribing in Primary Care: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: This systematic review evaluates the effect of audit and feedback (A&F) interventions targeting antibiotic prescribing in primary care and examines factors that may explain the variation in effectiveness. METHODS: Randomized controlled trials (RCTs) involving A&F interventions targeting antibiotic prescribing in primary care were included in the systematic review. Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, CINAHL, and ClinicalTrials.gov were searched up to May 2024. Trial, participant, and intervention characteristics were extracted independently by 2 researchers. Random effects meta-analyses of trials that compared interventions with and without A&F were conducted for 4 outcomes: (1) total antibiotic prescribing volume; (2) unnecessary antibiotic initiation; (3) excessive prescription duration, and (4) broad-spectrum antibiotic selection. A stratified analysis was also performed based on study characteristics and A&F intervention design features for total antibiotic volume. RESULTS: A total of 56 RCTs fit the eligibility criteria and were included in the meta-analysis. A&F was associated with an 11% relative reduction in antibiotic prescribing volume (N = 21 studies, rate ratio [RR] = 0.89; 95% confidence interval [CI]: .84, .95; I2 = 97); 23% relative reduction in unnecessary antibiotic initiation (N = 16 studies, RR = 0.77; 95% CI: .68, .87; I2 = 72); 13% relative reduction in prolonged duration of antibiotic course (N = 4 studies, RR = 0.87 95% CI: .81, .94; I2 = 86); and 17% relative reduction in broad-spectrum antibiotic selection (N = 17 studies, RR = 0.83 95% CI: .75, .93; I2 = 96). CONCLUSIONS: A&F interventions reduce antibiotic prescribing in primary care. However, heterogeneity was substantial, outcome definitions were not standardized across the trials, and intervention fidelity was not consistently assessed. Clinical Trials Registration. Prospero (CRD42022298297).
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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 | 0.027 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".