Pragmatic randomised trial assessing the impact of peer comparison and therapeutic recommendations, including repetition, on antibiotic prescribing patterns of family physicians across British Columbia for uncomplicated lower urinary tract infections
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of a personalised audit and feedback prescribing report (AF) and brief educational summary (ES) on empiric treatment of uncomplicated lower urinary tract infections (UTIs) by family physicians (FPs). DESIGN: Cluster randomised control trial. SETTING: The intervention was conducted in British Columbia, Canada between 23 September 2021 and 28 March 2022. PARTICIPANTS: We randomised 5073 FPs into a standard AF and ES intervention arm (n=1691), an ES-only arm (n=1691) and a control arm (n=1691). INTERVENTIONS: The AF contained personalised and peer-comparison data on first-line antibiotic prescriptions for women with uncomplicated lower UTI and key therapeutic recommendations. The ES contained detailed, evidence-based UTI management recommendations, incorporated regional antibiotic resistance data and recommended nitrofurantoin as a first-line treatment. MAIN OUTCOME MEASURES: Nitrofurantoin as first-line pharmacological treatment for uncomplicated lower UTI, analysed using an intention-to-treat approach. RESULTS: We identified 21 307 cases of uncomplicated lower UTI among the three trial arms during the study period. The impact of receiving both the AF and ES increased the relative probability of prescribing nitrofurantoin as first-line treatment for uncomplicated lower UTI by 28% (OR 1.28; 95% CI 1.07 to 1.52), relative to the delay arm. This translates to additional prescribing of nitrofurantoin as first-line treatment, instead of alternates, in an additional 8.7 cases of uncomplicated UTI per 100 FPs during the 6-month study period. CONCLUSION: AF prescribing data with educational materials can improve primary care prescribing of antibiotics for uncomplicated lower UTI. TRIAL REGISTRATION NUMBER: NCT05817253.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".