Developing a Knowledge Translation Intervention to Improve the Detection and Management of Pediatric Dyslipidemias in British Columbia
Bibliographic record
Abstract
Background: Familial hypercholesterolemia (FH) is a common, underdiagnosed genetic condition associated with premature cardiovascular disease. Despite the availability of Canadian Cardiovascular Society (CCS)/Canadian Pediatric Cardiology Association (CPCA) guidelines, awareness and uptake among primary care providers remain limited. We developed and evaluated a continuing medical education (CME) course to improve adherence to pediatric dyslipidemia guidelines across British Columbia. Methods: We conducted a quasiexperimental pre-/post-knowledge translation study. The CME course was delivered in-person at BC Children's Hospital and remotely to urban and rural family physicians and pediatricians. Pre-course and 1-month post-course surveys assessed self-reported confidence and adherence to CCS/CPCA recommendations. Results: < 0.001). Screening based on risk factors increased significantly: at-risk race and ethnicity (+41%), cardiometabolic conditions (+51%), early-onset high cholesterol (+46%), family history of diabetes (+26%), and premature cardiovascular events in first-degree relatives (+57%). Adherence to diagnostic recommendations improved, including dietary and exercise counseling (+31%), dietician referral (+29%), family history assessment (+46%), and lipid specialist referral (+36%). Treatment adherence also increased: cascade screening (+14%), statin initiation (+23%), dietician referral (+24%), and lipid specialist referral (+36%). Most participants (93%) agreed or strongly agreed that they acquired new knowledge and found the CME to be the most effective format for guideline dissemination. Conclusions: The CME course effectively promoted CCS/CPCA guideline uptake and improved self-reported clinical practices. Expanding delivery to include trainees, nurses, and pharmacists may enhance impact and reach.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".