Risk Prediction Models and Scores in Hypertrophic Cardiomyopathy
Bibliographic record
Abstract
Hypertrophic cardiomyopathy (HCM) has historically been linked with sudden cardiac death (SCD). Currently, it is well established that only a subset of patients is at the highest risk stratum for such a catastrophic event. Detection of patients belonging to this high-risk category can allow for timely defibrillator implantation, changing the natural history of HCM. Inversely, device implantation in patients deemed at low risk leads to an unnecessary burden of device complications with no apparent protective benefit. Previous studies have identified a series of markers, now considered established risk factors, with genetic testing and newer imaging allowing for the detection of novel, highly promising indices of increased risk for SCD. Despite the identification of a number of risk factors, there is noticeable discrepancy in the utility of such factors for risk stratification between the current American and European guidelines. We sought to systematically review the data available on these two approaches, presenting their rationale and respective predictive capacity, also discussing the potential of novel markers to augment the precision of currently used risk stratification models for SCD in HCM.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".