Relationships Between Baseline Q Waves, Time From Symptom Onset, and Clinical Outcomes in ST-Segment–Elevation Myocardial Infarction Patients
Bibliographic record
Abstract
Background— Using a comprehensive ST-segment–elevation myocardial infarction registry, we evaluated the relationships of baseline Q waves, time from symptom onset, and reperfusion strategy with in-hospital clinical outcomes. Methods and Results— Consecutive ST-segment–elevation myocardial infarction patients from a defined health region were classified by the presence of baseline Q waves and additionally into primary percutaneous coronary intervention, fibrinolysis, or no reperfusion. ECGs were collected at baseline, after reperfusion, and analyzed for the presence of Q waves using Selvester criteria. Among 2290 ST-segment–elevation myocardial infarction patients, 36.9% had Q waves on their baseline ECG. Patients with Q waves were older (median age, 59 versus 57), were more often male (82.0% versus 75.4%), had higher heart rate (80 versus 72), had higher Global Registry of Acute Coronary Events risk score (129 versus 127), and were with longer time to reperfusion (42 minutes longer). They had higher composite end points (16.3% versus 10.0%), consistent across times from symptom onset to presentation (15.4% versus 9.9% ≤3 hours; 18.5% versus 8.9% >3 to ≤6 hours; 15.9% versus 11.3% >6 hours; Q and no Q, respectively). Baseline Q waves, but not time to reperfusion, were associated with an increased odds of the in-hospital composite end point of death, congestive heart failure, cardiogenic shock, and reinfarction (adjusted odds ratio, 1.65; 95% confidence interval, 1.18–2.30; P =0.003). Type of reperfusion did not modify the association of baseline Q waves and in-hospital outcomes ( P interaction=0.918). Conclusions— The presence of baseline Q waves, rather than time to treatment, was significantly associated with adverse in-hospital events in real-world patients, regardless of reperfusion strategy used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".