Feedback of Antibiotic Prescribing in Primary Care (FAPPC) trial: results of a real-world cluster randomized controlled trial in Scotland, UK
Bibliographic record
Abstract
OBJECTIVES: To evaluate the effect of general practice-level prescribing feedback on antibiotic prescribing in a real-world pragmatic cluster randomized controlled trial. METHODS: Three hundred and forty general practices in four territorial Health Boards in NHS Scotland were randomized in Quarter 1, 2016 to receive four quarterly antibiotic-prescribing feedback reports or not, from Quarter 2, 2016 to Quarter 1, 2017. Reports included different clinical topics, benchmarking against national and health board rates, and behavioural messaging with improvement actions. The primary outcome was total antibiotic prescribing rate. There were 16 secondary prescribing outcomes and 5 hospital admission outcomes (potential adverse effects of reduced prescribing). The main evaluation timepoint was 1 year after the final report (Quarter 1, 2018), with an additional evaluation in the quarter after the final report (Quarter 2, 2017). Routine administrative NHS data were used to generate the feedback reports and analyse the effects. RESULTS: Total antibiotic prescribing rates were lower at the main evaluation timepoint in both intervention (1.83 versus baseline 1.93 prescriptions/1000 patients/day) and control (1.90 versus baseline 1.98) practices, with no evidence of intervention effect [adjusted rate ratio (ARR) 0.98 (95% CI 0.94-1.02; P = 0.35)]. At the additional timepoint, adjusted total antibiotic prescribing rates were 1.67 and 1.73 prescriptions/1000 patients/day, with evidence of a small intervention effect, ARR 0.99 (0.98-1.00; P = 0.03). CONCLUSIONS: This well-designed, practice-level antibiotic-prescribing feedback had limited evidence of additional effects in the context of decreasing antibiotic prescribing and an established national stewardship programme.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".