Long Term Effectiveness on Prescribing of Two Multifaceted Educational Interventions: Results of Two Large Scale Randomized Cluster Trials
Bibliographic record
Abstract
INTRODUCTION: Information on benefits and risks of drugs is a key element affecting doctors' prescribing decisions. Outreach visits promoting independent information have proved moderately effective in changing prescribing behaviours. OBJECTIVES: Testing the short and long-term effectiveness on general practitioners' prescribing of small groups meetings led by pharmacists. METHODS: Two cluster open randomised controlled trials (RCTs) were carried out in a large scale NHS setting. Ad hoc prepared evidence based material were used considering a therapeutic area approach--TEA, with information materials on osteoporosis or prostatic hyperplasia--and a single drug oriented approach--SIDRO, with information materials on me-too drugs of 2 different classes: barnidipine or prulifloxacin. In each study, all 115 Primary Care Groups in a Northern Italy area (2.2 million inhabitants, 1737 general practitioners) were randomised to educational small groups meetings, in which available evidence was provided together with drug utilization data and clinical scenarios. Main outcomes were changes in the six-months prescription of targeted drugs. Longer term results (24 and 48 months) were also evaluated. RESULTS: In the TEA trial, one of the four primary outcomes showed a reduction (prescription of alfuzosin compared to tamsulosin and terazosin in benign prostatic hyperplasia: prescribing ratio -8.5%, p = 0.03). Another primary outcome (prescription of risedronate) showed a reduction at 24 and 48 months (-7.6%, p = 0.02; and -9,8%, p = 0.03), but not at six months (-5.1%, p = 0.36). In the SIDRO trial both primary outcomes showed a statistically significant reduction (prescription of barnidipine -9.8%, p = 0.02; prescription of prulifloxacin -11.1%, p = 0.04), which persisted or increased over time. INTERPRETATION: These two cluster RCTs showed the large scale feasibility of a complex educational program in a NHS setting, and its potentially relevant long-term impact on prescribing habits, in particular when focusing on a single drug. National Health systems should invest in independent drug information programs. TRIAL REGISTRATION: Controlled-Trials.com ISRCTN05866587.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".