Resolution of ST-segment depression: a new prognostic marker in ST-segment elevation myocardial infarction
Bibliographic record
Abstract
AIMS: To evaluate the prognostic impact of ST depression resolution among patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary PCI in the Assessment of Pexelizumab in Acute Myocardial Infarction (APEX-AMI) trial. METHODS AND RESULTS: In this study, 4729 of 5745 patients had analysable ECGs demonstrating concomitant ST-segment depression. Resolution of summation operatorST elevation (STE-R) and summation operatorST depression (STD-R) on 30 min post-PCI ECGs was dichotomized into those with > or =50 vs. <50% ST-segment resolution. Overall, 1143 patients (24%) had STD-R<50%. These patients had higher risk characteristics including older age, female sex, diabetes, hypertension, prior CHF/MI, Killip class >I, triple vessel disease, and less frequent TIMI 3 flow in the culprit coronary vessel post-PCI. After multivariable adjustment and accounting for STE-R, STD-R<50% remained an independent predictor for 90 day death and the composite of death, cardiogenic shock, or CHF. When compared with patients with both STE-R and STD-R> or =50%, patients with both STE-R and STD-R<50% had the worst outcomes [hazard ratios (HR) 90 day death: 2.54; 95% confidence intervals (CI): 1.71-3.77; HR 90 day composite: 2.18; 95% CI: 1.63-2.91]. CONCLUSION: When ST depression is present in STEMI patients undergoing primary PCI, STD-R<50% provides independent prognostic value that is incremental to STE-R.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".