Toronto Hypertrophic Cardiomyopathy Genotype Score for Prediction of a Positive Genotype in Hypertrophic Cardiomyopathy
Bibliographic record
Abstract
BACKGROUND: Genotyping in hypertrophic cardiomyopathy has gained increasing attention in the past decade. Its major role is for family screening and rarely influences decision-making processes in any individual patient. It is associated with substantial costs, and cost-effectiveness can only be achieved in the presence of high-detection rates for disease-causing sarcomere protein gene mutations. Therefore, our aim was to develop a score based on clinical and echocardiographic variables that allows prediction of the probability of a positive genotype. METHODS AND RESULTS: Clinical and echocardiographic variables were collected in 471 consecutive patients undergoing genetic testing at a tertiary referral center between July 2005 and November 2010. Logistic regression for a positive genotype was used to construct integer risk weights for each independent predictor variable. These were summed for each patient to create the Toronto hypertrophic cardiomyopathy genotype score. A positive genotype was found in 163 of 471 patients (35%). Independent predictors with associated-risk weights in parentheses were as follows: age at diagnosis 20 to 29 (-1), 30 to 39 (-2), 40 to 49 (-3), 50 to 59 (-4), 60 to 69 (-5), 70 to 79 (-6), ≥80 (-7); female sex (4); arterial hypertension (-4); positive family history for hypertrophic cardiomyopathy (6); morphology category (5); ratio of maximal wall thickness:posterior wall thickness <1.46 (0), 1.47 to 1.70 (1), 1.71 to 1.92 (2), 1.93 to 2.26 (3), ≥2.27 (4). The model had a receiver operator curve of 0.80 and Hosmer-Lemeshow goodness-of-fit P=0.22. CONCLUSIONS: The Toronto genotype score is an accurate tool to predict a positive genotype in a hypertrophic cardiomyopathy cohort at a tertiary referral center.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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".