Childhood dyslipidemia: Clinician management practices in the primary care setting
Bibliographic record
Abstract
Objectives: To describe clinician management practices for childhood dyslipidemia in the community setting. Methods: A descriptive study was conducted for children aged 2 to 10 years with dyslipidemia as defined by the National Cholesterol Education Program Expert Panel on Cholesterol in Children criteria. A convenience sample of participants from the TARGet Kids! cohort study (Toronto, Canada) was used. Trained research assistants reviewed participant medical records to document clinician management practices of abnormal lipid levels. The study outcome was the proportion of clinicians engaging in each management practice. Descriptive statistics were completed, reporting the proportion of clinician engagement in management practices. Results: All 768 children were seen by primary paediatric care providers after lipid levels identified dyslipidemia. Medical history regarding lifestyle behaviours and cardiovascular risk factors were frequently obtained (n = 565, 73.6%). Physicians rarely informed families about abnormal lipid levels (n = 11, 1.43%). Management plans for abnormal lipid levels were rarely documented (n = 4, 0.5%). Clinicians did not refer to paediatric lipid specialists or initiate statin therapy. Conclusions: Paediatric care providers rarely identified and initiated early management for abnormal lipid levels. Our results may inform the need for improved knowledge translation of the recently published Canadian clinical practice update for the detection and management of childhood dyslipidemia.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".