Risk stratification of patients with familial hypercholesterolemia in a multi-ethnic cohort
Bibliographic record
Abstract
BACKGROUND: Heterozygous Familial hypercholesterolemia (FH) is a common autosomal dominant disorder resulting in in very high blood cholesterol levels and premature cardiovascular disease (CVD). However, there is a wide variation in the occurrence of CVD in these patients. The aim of this study is to determine risk factors that are responsible for the variability of CVD events in FH patients. METHODS: This is a retrospective analysis of a large multiethnic cohort of patients with definite FH attending the Healthy Heart Prevention Clinic in Vancouver, Canada. Cox proportional hazard regression analysis was used to assess the association of the risk factors to the hard cardiovascular outcomes. RESULTS: 409 patients were identified as having "definite" FH, according to the Dutch Lipid Clinic Network Criteria (DLCNC), with 111 (27%) having evidence of CVD. Male sex, family history of premature CVD, diabetes mellitus, low high density lipoprotein cholesterol (HDL-C) and high lipoprotein (a) (Lp (a)) were significant, independent risk factors for CVD. In men, family history, diabetes and low levels of HDL-C were significant risk factors while in women smoking, diabetes mellitus and high Lp (a) were significant risk factors for CVD. There were no significant differences in risk factors between ethnicities. CONCLUSION: In conclusion, men and women differ in the impact of the risk factors on the presence of CVD with family history of CVD and low HDL-C being a significant factor in men while smoking and increased Lp (a) were significant factors in women. Diabetes was a significant factor in both men and women.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".