Abstract 3415: Cardiovascular Magnetic Resonance Accurately Detects Myocardial Hemorrhage in Reperfusion Injury
Bibliographic record
Abstract
Introduction Hemorrhage can complicate reperfusion injury in myocardial infarction, but no in vivo imaging method exists. We hypothesized that a T2*-weighted cardiovascular magnetic resonance imaging (CMR) sequence accurately detects myocardial hemorrhage in vivo. Methods Reperfused myocardial infarcts were generated in 14 dogs by ligation of the LAD for 3– 6 hours. At day 3, a CMR study was performed using established sequences for quantification of function, microvascular obstruction and myocardial infarction in short axis slices covering the entire myocardium. For hemorrhage, a T2*-weighted multi-echo gradient-echo sequence was applied (TE=35msec, TR=1 RR, FOV 380x280mm, matrix 256 x 192). A segmental analysis was performed using 6 segments per slice: T2*-segments were considered hemorrhagic when signal intensity was >2 standard deviations below the mean of remote myocardium. The contrast-to-noise ratio of hemorrhagic segments over normal segments was assessed. Post-mortem TTC staining was performed with infarcted and hemorrhagic areas analyzed with the same segmental approach. In vivo/ ex vivo correlation statistics were performed. Results In 14 dogs, 378 segments were assessed. Of the T2* CMR segments, 10.2% were excluded due to artifacts; 27 segments yielded a signal drop consistent with hemorrhage (see figure ). The correlation with ex vivo TTC staining was R=0.79 and contrast-to-noise ratio was 9.2±3.4. Sensitivity to detect a hemorrhagic segment was 77%, specificity 99%. Conclusion T2*-weighted CMR accurately detects reperfusion hemorrhage in vivo. The method may be applied in patients to study hemorrhage as a complication of reperfusion injury.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".