Pilot study for appropriate anti-infective community therapy. Effect of a guideline-based strategy to optimize use of antibiotics.
Bibliographic record
Abstract
OBJECTIVE: To determine whether a community-wide, multi-intervention educational strategy (CoMPLI model) could enhance adoption of clinical guidelines and improve the use of antibiotics. DESIGN: Before-after trial using baseline and study periods with a control group. SETTING: A small community in central Ontario. PARTICIPANTS: Health professionals, the general public, and the pharmaceutical industry. INTERVENTIONS: The educational strategy (CoMPLI), carried out during 6 winter months, consisted of continuing medical education sessions for health professionals and pharmaceutical representatives and a parallel public education campaign that included town hall meetings and pamphlets distributed by local pharmacists. The two main messages were: do not use antibiotics for viral respiratory infections, and use drugs recommended in the publication, Anti-infective Guidelines for Community-Acquired Infections. MAIN OUTCOME MEASURES: Total number of antibiotic claims and adjusted odds ratios (OR) were used to measure the likelihood of physicians prescribing first- or second-line agents compared with the previous year and compared with control physicians. RESULTS: Claims in the study community decreased by nearly 10% during the 6-month study period compared with the baseline period from the previous year. Study physicians were 29% less likely (OR-1 = 0.71, range 0.67 to 0.76) to prescribe second-line antibiotics during the study period than physicians in the rest of the province. CONCLUSIONS: Physicians participating in the pilot study were more likely to follow drug recommendations outlined in published guidelines.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".