A cluster randomized trial assessing the impact of personalized prescribing feedback on antibiotic prescribing for uncomplicated acute cystitis to family physicians
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of personalized prescribing portraits on antibiotic prescribing for treating uncomplicated acute cystitis (UAC) by Family Physicians (FPs). DESIGN: Cluster randomized control trial. SETTING: The intervention was conducted in the primary care setting in the province of BC between December 2010 and February 2012. PARTICIPANTS: We randomized 4 833 FPs by geographic location into an Early intervention arm (n = 2 417) and a Delayed control arm (n = 2 416). INTERVENTION: The Education for Quality Improvement in Patient Care (EQIP) program mailed to each FP in BC, a 'portrait' of their individual prescribing of antibiotics to women with UAC, plus therapeutic recommendations and a chart of trends in antibiotic resistance. MAIN OUTCOME MEASURES: Antibiotic prescribing preference to treat UAC. RESULTS: Implementing exclusion criteria before and after a data system change in the Ministry of Health caused the arms to be unequal in size-intervention arm (1 026 FPs, 17 637 UAC cases); control arm (1 352 FPs, 25 566 UAC cases)-but they were well balanced by age, sex and prior rates of prescribing antibiotics for UAC. In the early intervention group probability of prescribing nitrofurantoin increased from 28% in 2010 to 38% in 2011, a difference of 9.9% (95% confidence interval [CI], 9.1% to 10.7. Ciprofloxacin decreased by 6.2% (95% CI: 5.6% to 6.9%) and TMP-SMX by 3.7% (95% CI: 3.1% to 4.2%). Among 295 FPs who completed reflective surveys, 52% said they were surprized by the E. coli resistance statistics and 57% said they planned to change their treatment of UAC. CONCLUSION: The EQIP intervention demonstrated that feedback of personal data to FPs on their prescribing, plus population data on antibiotic resistance, with a simple therapeutic recommendation, can significantly improve prescribing of antibiotics. Trial registration: ISRCTN 16938907.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".