Relationship between myocardial edema and left ventricular wall thickness in acute myocardial ischemia: A Magnetic Resonance Imaging study
Bibliographic record
Abstract
Purpose: We sought to assess the relationship between left ventricular regional end-diastolic myocardial wall thickness (EDWT) and myocardial edema defined using T2-weighted Cardiovascular Magnetic Resonance (CMR) after acute myocardial ischemia and reperfusion. Methods: T2-weighted and cine CMR images for 7 dogs at baseline, during coronary occlusion (mean 33 ± 4 minutes) and after reperfusion were studied. The EDWT was measured in segments with high signal intensity (SI) on T2-weighted images, adjacent segments and remote segments according to a 16-segment model. Results: The EDWT after reperfusion in segments with high SI on T2-weighted images was significantly increased compared to baseline (6.28 ± 1.06 mm and 5.51 ± 1.40 mm, p < 0.05), whereas EDWT after the reperfusion in adjacent and remote segments did not show significant difference compared to baseline (adjacent: 6.48 ± 1.55 mm and 6.38 ± 1.26 mm, p = N.S., remote: 6.41 ± 1.11mm and 6.42 ± 1.27mm, p = N.S.). The % increase in EDWT after reperfusion from baseline in segments with high SI on T2-weighted images was higher than those in adjacent and remote segments (19 ± 30%, 1.3 ± 15% and 1.5 ± 16%, respectively, p < 0.05). Conclusions: After a brief period of ischemia and reperfusion, edema as defined by high SI on T2-weighted CMR is related to an increase in EDWT. This increase however is too small to be clinically relevant to be used for the detection of acute myocardial injury. Edema imaging is more sensitive and is an essential part of the reliable assessment of acute ischemic myocardial 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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".