Canadian and French Risk Scores Are Valid in Identifying Cardiovascular Disease in Australian Patients With Familial Hypercholesterolemia
Bibliographic record
Abstract
BACKGROUND: Familial hypercholesterolemia (FH) is a co-dominantly inherited condition that leads to enhanced risk of atherosclerotic cardiovascular disease (ASCVD). The Montreal FH Score (MFHS), Combined FH Score (CFHS), and FH Risk Score (FHRS) are strongly associated with ASCVD events in patients with heterozygous FH (HeFH). In this study, the association between these risk scores and prevalent ASCVD was evaluated among Australian patients with HeFH. METHODS: We collected clinical data from 655 adult patients with genetically confirmed HeFH (87% with LDLR, 11% with APOB, and 2% with PCSK9 or APOE p.Leu167del variants). Logistic regression was used to assess the association between risk scores and prevalence of ASCVD. Receiver operating characteristic curve analysis was used to evaluate the discriminatory ability of the risk scores. RESULTS: We identified 153 patients with a history of ASCVD events. A 1-unit increase in the MFHS, CFHS, and FHRS was associated with 16%, 18%, and 14% increase in the odds of ASCVD, respectively. Patients with high (greater than the median) MFHS (≥ 25), CFHS (≥ 26), and FHRS (≥ 31) had 9.7-fold, 9.1-fold, and 13.4-fold greater odds of ASCVD compared with those with low scores. The area under the receiver operating characteristic curve for MFHS, CFHS, and FHRS were 0.808 (95% confidence interval [CI], 0.772-0.844), 0.821 (95% CI, 0.785-0.856), and 0.818 (95% CI, 0.782-0.854), respectively, indicating excellent discriminatory ability, with the area under the receiver operating characteristic curve for CFHS being significantly higher than for the MFHS (P = 0.014). CONCLUSIONS: The MFHS, CFHS, and FHRS were strongly associated with an increase in the prevalence of ASCVD, with excellent discriminatory ability in identifying ASCVD in Australian patients with HeFH.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".