© 2009 Canadian Medical Association or its licensors
Bibliographic record
Abstract
Despite the abundant evidence base for the secondaryprevention of coronary artery disease,1 many ofthese therapies are underused in clinical practice.1–4 These gaps between evidence and clinical reality are linked to poor outcomes for patients.5 Improved uptake of secondary-prevention therapies would reduce cardiac mor-bidity and mortality.6 However, most quality-improvement initiatives in coronary artery disease have focused on patients in hospital. Few studies have evaluated means of translating evidence into clinical practice for outpatients cared for by primary care physicians.7 Previously,8 we developed and tested the Local Opinion Leader Statement (Appendix 1, available at www.cmaj.ca /cgi/content/full/cmaj.090917/DC1), a quality-improvement tool consisting of a 1-page summary of evidence with explicit treatment advice about secondary prevention of coronary artery disease. This summary was endorsed by local opinion leaders and was faxed to the primary care physicians of patients with coronary artery disease. Although this fax did not lead to a significant improvement in statin prescribing, our pilot trial was small (117 patients) and enrolled patients with chronic coronary artery disease at the time they presented to their community pharmacy for medication refills. We hypoth-esized that this was not a “teachable moment ” and that if the intervention was given at a time when the diagnosis was made, it would be more influential on the primary care physician. Thus, we designed this trial to test the impact of the opinion leader statement if it was sent to the primary physician at the time when patients were diagnosed with coronary artery disease. In addition, because local opinion leaders are not always self-evident and conducting surveys to identify them for each condi-tion and in each locale would be time-consuming and expen-sive, we also evaluated the impact of an unsigned statement. Methods
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.822 | 0.671 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".