Abstract 3162: Dynamic mortality modeling in PCI-treated ST-elevation Myocardial Infarction: Implications for Clinical Decision Making
Bibliographic record
Abstract
Background: Baseline factors predict adverse events after acute myocardial infarction (AMI), whereas dynamic risk modeling captures its evolving nature using continuous temporal profile updating. This refinement may guide medical decisions. Methods: APEX AMI trial tested pexelizumab efficacy in 5745 STEMI pts undergoing primary PCI; no treatment effect was observed. Four multivariable Cox models predicted 90-day mortality from key time points: baseline, 30-min post-PCI, 24 hrs, 96 hrs. % ST-resolution 30-min post-PCI, TIMI Flow pre-and post-PCI were measured. Complications included shock, CHF, re-MI, recurrent ischemia, stroke, renal failure, severe bleeding and major cardiac rhythm disturbances. The relative covariate contribution was quantified as the % of the ∑individual X 2 ’s. Results: Ninety-day mortality was 4.7%; 22% and 40% of deaths occurred < 24 hrs and 4 days, respectively. The figure (left ) shows decreasing relative contributions of age, systolic BP/heart rate and other factors over time to 90d mortality, and incremental influence of in-hospital complications, TIMI flow and post-PCI ST-resolution. As time elapsed, the % of “low risk” pts increased. (Right panel) At 96 hrs, a possible early discharge point for low risk pts, 44.0% of survivors had been discharged. This discharge rate decreased with increasing risk: Low: 53.3%; Moderate: 35.3%; High: 20.4% (p < 0.001). If early discharge depended on risk, the remaining 46.7% of low risk pts might have been discharged early. Conclusions: This novel approach demonstrates the dynamic nature of STEMI risk over time and may be used to guide early clinical decision-making.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".