A Community-Based Educational Intervention to Improve Antithrombotic Drug Use in Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Despite evidence that antithrombotics are effective in reducing the risk of stroke in atrial fibrillation (AF), they remain underused. OBJECTIVE: To perform a controlled trial of a comprehensive educational program promoting the rational prescribing of antithrombotics for stroke prevention in AF. METHODS: The intervention was conducted in Southern Tasmania, Australia, using Northern Tasmania as a control area. General practitioners were sent locally produced guidelines on stroke risk stratification and antithrombotic drug use in AF, which were followed by academic detailing visits. Outcomes were measured using evaluation feedback from the general practitioners, and drug utilization data were provided by a series of patients presenting to the hospital with an admission diagnosis of AF and dispensing of antithrombotic therapy under the Australian Pharmaceutical Benefits Scheme. RESULTS: During the educational intervention, 272 guidelines were mailed and, subsequently, 162 general practitioners were visited and the guidelines discussed. Hospital admission data before and after the intervention revealed a significant increase in the use of warfarin in patients at high risk of stroke (33% vs 46% of eligible patients; p < 0.05). Analysis of prescription data for warfarin also indicated that the increase in use of warfarin within the intervention region was significantly greater than for the control region (p < 0.001). CONCLUSIONS: The educational program described here led to a significant increase in the prescribing of warfarin for stroke prevention in patients with AF.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".