Abstract 13047: Clinical Risk Stratification for Primary Prevention Implantable Cardioverter Defibrillators (ICDs)
Bibliographic record
Abstract
Introduction: Clinical risk stratification may refine decision-making regarding primary prevention ICDs and provide a comparator for advanced diagnostic tests for prediction of sudden cardiac death. Objective: To identify predictors of appropriate ICD shock competing with mortality using clinical variables. Methods: We studied a prospective, multicenter, population-based cohort with LVEF ≤35% referred for primary prevention ICD in Ontario, Canada. Patients were followed for appropriate ICD shocks at 18 device follow-up centers and for survival via vital status registry. We used a Fine-Gray subdistribution hazard model to develop a risk score for simultaneous prediction of appropriate ICD shock and death. Results: Among 7020 referred, 3445 pts underwent primary prevention ICD implant (80% men, 66 yrs [IQR: 58-73]). During 5918 person-years (PY) follow-up, there were 204 pts with appropriate shock (3.6 per 100 PY) and 292 deaths (4.9 per 100 PY). Competing risk predictors of appropriate shock included nonsu...
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".