Abstract 10748: Development and Internal Validation of the Coronary Revascularization-Tool for Evidence-Based Individualized Share Decision-Making (CR-DECIDE) Major Adverse Cardiovascular Events Model
Bibliographic record
Abstract
Introduction: Coronary revascularization guidelines emphasize shared decision-making (SDM) for chronic CAD management. Decision aids that provide individualized estimates of benefits & risks could improve the relevance & evidence base of SDM. This study sought to develop & internally validate a clinical prediction model for 3-year MACE (death/MI/stroke) in patients with chronic CAD for integration into a point-of-care decision aid. Methods: We used the prospective Cardiac Services British Columbia Registry to assemble a cohort of adults with stable angina & obstructive CAD who underwent first diagnostic coronary angiography between 2004-2015, excluding left main disease, LVEF <30%, prior MI/PCI/CABG, NYHA 3-4, & CCS class 4 angina for model derivation. Internal validation was performed with 200 bootstrap samples & missing data was handled by multiple imputation. We included 22 candidate variables with 1 interaction term, based on prior literature, in a Cox proportional-hazards model. We assessed model discrimination & calibration using Harrell’s c-index & the calibration slope, respectively. Results: We included 24,990 participants (2026 events). We kept 22 predictors in the final model; the interaction term was removed since it was not significant (p=0.46). Due to non-proportionality, pLAD stenosis & angiographer’s recommended treatment were used as stratification variables (6 strata). Optimism-corrected c-index was 0.69 & calibration slope was 0.97. Conclusions: The CR-DECIDE model had moderate ability to predict MACE in patients with chronic CAD, & warrants further validation in an external dataset & impact analysis as part of a point-of-care decision aid.
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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 | 0.020 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".