Increasing likelihood of prescribing recommended lipid management
Bibliographic record
Abstract
OBJECTIVE: To explore whether participation in a series of cardiology continuing medical education (CME) activities affects primary care providers' (PCPs') lipid management for their patients. DESIGN: This retrospective cohort study used a database of participation in cardiology CME activities (2011 to 2017) linked to electronic medical records. Statistical analyses were completed using logistic regression with generalized estimating equations. SETTING: Manitoba. PARTICIPANTS: Patients receiving care from 225 PCPs participating in the Manitoba Primary Care Research Network. MAIN OUTCOME MEASURES: Recommended lipid management was defined as prescription of statins (yes or no) among patients diagnosed with cardiovascular disease (CVD), patients diagnosed with diabetes mellitus (DM; 40 years or older), and patients diagnosed with chronic kidney disease (CKD; 50 years and older) in 2017. Treatment was identified using the ATC (Anatomical Therapeutic Chemical) system (ATC code C10AA or C10B). RESULTS: After adjusting for relevant confounders, the odds of prescribing statins to patients with CVD, DM, or CKD among PCPs who did not participate in the cardiology CME activities were 50%, 55%, and 67% lower, respectively, than among PCPs who participated in 2 or more activities. The odds of prescribing statins to patients with CVD and DM among PCPs who participated in only 1 cardiology CME activity were also 67% and 63% lower, respectively, than among PCPs who participated in 2 or more activities. CONCLUSION: Results suggested that PCPs who participated in 2 or more cardiology CME activities were more likely to prescribe recommended lipid management (statins) for adults with CVD, DM, or CKD.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".