Evaluating the prevalence of lipid assessments in children in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Familial hypercholesterolemia is a common, inherited, life-threatening and treatable condition that is characterized by marked elevations of low-density lipoprotein cholesterol (LDL-C), resulting in a high risk of cardiovascular disease, but treatment starting in childhood dramatically reduces this risk. We sought to evaluate the prevalence of pediatric lipid assessments among children in Alberta. METHODS: We reviewed laboratory and administrative data from Alberta Health between Apr. 1, 2012, and Dec. 31, 2021. We evaluated 2 pediatric cohorts (children aged 2-10 yr and children aged 9-17 yr) to allow for longitudinal assessments throughout the pediatric period. We also reviewed annual frequencies of lipid assessment for all children between 2013 and 2021. RESULTS: Pediatric lipid assessments were performed for 1972 (4.3%) of 46 170 children aged 2-10 years and for 8158 (19.9%) of 40 926 children aged 9-17 years. Female children (aged 2-10 yr) and those living in rural communities were significantly less likely to have a lipid assessment, compared with male children and those in nonrural communities. Among those with lipid assessments, 23 (1.2%) and 86 (1.1%) children aged 2-10 years and 9-17 years, respectively, had an LDL-C level suggestive of probable familial hypercholesterolemia (≥ 4.0 mmol/L). Statin therapy was prescribed in 16 children during the study period. The frequency of lipid assessments was relatively stable, with the exception of a decrease in 2020. INTERPRETATION: Rates of pediatric lipid assessment in Alberta are suboptimal. These findings highlight the need to increase awareness of the benefits of early diagnosis and treatment of familial hypercholesterolemia with regard to long-term health and identify and overcome barriers to diagnosis and treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".