Abstract 2419: The Presence of Q Waves, QRS Fragmentation or QRS Duration Does Not Correlate with Scar Volume in Patients with Ischemic Cardiomyopathy.
Bibliographic record
Abstract
Background. Recent studies have suggested that scar volume is one of the predictors of ICD utilization. We hypothesized that noninvasive ECG predictors of myocardial scar (number of leads with Q waves and/or fragmented QRS complexes, fQRS) would correlate with infarct size and could predict patients with a depressed ejection fraction that would be the most likely to benefit from imaging scar volume. Methods. Patients with ischemic cardiomyopathy eligible for an ICD for the primary prevention of sudden death underwent PET imaging (n=78). Scar volume (% LV) was quantified from 18 FDG uptake during insulin stimulation and 13 N-ammonia flow using a validated algorithm (MyoPC, Ottawa Heart Institute). Pathologic Q waves and fQRS (RSR morphology or notching in R or S waves) on the 12-lead ECG were assessed by consensus of three blinded readers. Results. Subjects were 67 ± 12 years of age and 87% male. Average ejection fraction was 28 ± 10%. Myocardial scar encompassed 17.1 ± 7.3% of the left ventricle, with a very wide range among subjects (1.9 to 34.4%). In patients with a QRS duration <120 msec (n=47), there was very poor correlation between scar volume and the number of leads with Q waves, fQRS or both (R 2 =0.01– 0.06, Table ). Furthermore, patients with a wide QRS (>120 msec) did not have an increase in scar volume (Table ). Conclusions. These results indicate that 1.) The volume of scar varies widely in patients with ischemic cardiomyopathy that are eligible to receive an ICD for primary prevention and 2.) Infarct volume is independent of electrocardiographic indices of scar. Thus, imaging is necessary to stratify risk for SCD as a function of scar volume.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".