Montreal-FH-SCORE Predicts Coronary Artery Calcium Score in Patients With Familial Hypercholesterolemia
Bibliographic record
Abstract
Background Familial hypercholesterolemia (FH) is a monogenic disease characterized by a high concentration of low-density lipoprotein cholesterol. This population is considered to be at high cardiovascular risk; however, disease evolution remains heterogeneous among individuals. The coronary artery calcium (CAC) score is currently the best predictor of incidental major cardiovascular events in primary prevention in the general population. Few studies have described the CAC score in FH populations. Methods The objective of our study was to determine the predictors of the CAC score in FH patients. We retrospectively studied FH patients followed at the Montreal Clinical Research Institute (IRCM) Lipid Clinic who had a cardiac scan for CAC score, using the Agatston method, between 2013 and 2019. Results Final analysis included 62 FH patients. Mean age was 48 ± 14 years old, and 48% were men. Overall, 25 patients had a CAC score of 0 (40%), and 37 patients had a nonzero CAC score (60%). Sex, age, Montreal-FH-SCORE (MFHS), waist circumference, and statin exposure in years were significant predictors ( P ≤ 0,05) of a nonzero CAC score in a univariate model. MFHS was the only factor that remained significant in a multivariate model (odds ratio 1.34, 95% confidence interval 1.11–1.61, P = 0.002). Conclusions In conclusion, we found that MFHS, which includes traditional cardiovascular risk factors, was a predictor of a nonzero CAC score in FH patients. This finding suggests that MFHS may play a role in determining the cardiovascular risk and therefore the intensity of treatment in FH patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".