Current practices, attitudes and barriers to paediatric lipid screening and management amongst Family Physicians and Paediatricians in British Columbia
Bibliographic record
Abstract
Objective: To determine current practices, attitudes and barriers to pediatric lipid screening and management amongst family physicians and paediatricians in British Columbia (BC). Design: Cross-sectional online survey. Setting: Urban, suburban, and remote locations across BC. Participants: Community-based or academic family physicians and paediatricians. Main outcome measures: Self-reported lipid screening practices in children and diagnosis and management of dyslipidemia. Secondary outcomes were self-reported attitudes and barriers to screening and treatment. Results: Most physicians are not screening their pediatric patients for dyslipidemias (85% of family physicians, 84% of paediatricians) and disagreed with screening children for elevated cholesterol both pre-puberty (67% of family physicians, 79% of paediatricians) and in adolescence (67% of family physicians and paediatricians). While 77% of paediatricians and 56% of family physicians agreed that statins are an appropriate treatment for children with Familial hypercholesterolaemia (FH), most respondents (89% of family physicians, 76% of paediatricians) reported they would not prescribe them. The most widely cited barrier to screening and management of paediatric dyslipidemias was a lack of Canadian guidelines (74% of family physicians, 53% of paediatricians). Conclusions: Despite the recent release of a clinical practice update by the Canadian Cardiovascular Society and Canadian Pediatric Cardiology Association, awareness of recommendations for the detection and management of pediatric dyslipidemias amongst family physicians and paediatricians is low, and practices remain at odds with these recommendations. Knowledge translation interventions are necessary to disseminate clinical practice recommendations to physicians and evoke practice change.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".