Failure of investigator adherence to electrocardiographic entry criteria is frequent and influences clinical outcomes: lessons from APEX-AMI
Bibliographic record
Abstract
AIMS: To examine the extent and impact on clinical outcomes of adherence to electrocardiogram (ECG) entry criteria in ST-elevation myocardial infarction patients in the assessment of pexelizumab in acute myocardial infarction (APEX-AMI) trial. METHODS AND RESULTS: We examined the frequency, characteristics, and outcomes of patients enrolled in APEX-AMI trial who did not meet the trial ECG entry criteria. Among 5615 patients analysed, 28.8% did not meet ECG entry criteria: this occurred more than twice as frequently amongst those with high-risk inferior vs. those with other MI (42.3 vs. 19.3%, P < 0.001). Regardless of infarct location, patients who failed to meet ECG entry criteria had significantly lower mortality (2.5 vs. 4.5% at 30 days and 3.1 vs. 5.3% at 90 days; both P < 0.001) and the composite rate of death, cardiogenic shock, or CHF (5.8 vs. 10.3% at 30 days and 6.9 vs. 11.4% at 90 days; both P < 0.001) as compared to those who met criteria. CONCLUSION: In APEX-AMI over one-quarter of enrolled patients did not meet ECG entry criteria and had better outcomes than eligible patients. Although the trial's primary result was unaffected by alignment with the baseline ECG criteria, our findings may have important implications in designing future trials.
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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 | 0.030 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".