Detection of papillary muscle infarction by late gadolinium enhancement: incremental value of short-inversion time vs. standard imaging
Bibliographic record
Abstract
AIMS: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can detect myocardial scar in patients with myocardial infarction. The detection of papillary muscle infarction (PMI) may be difficult due to the bright blood signal. The aim of our study was to evaluate the incremental value of LGE CMR imaging using an inversion recovery (IR)-GRE with a short-inversion time (TI) over standard LGE imaging in identifying PMI. METHODS AND RESULTS: Fifty-six patients with myocardial infarction were studied using a standard IR-GRE LGE sequence with an adjusted TI to null the signal intensity of normal myocardium and with a 3D IR-GRE with a short TI (<180 ms). Signal-to-noise and contrast-to-noise ratios (CNR) and the frequency of PMI were determined. Image quality and infarction sharpness were evaluated. The short-TI LGE sequence detected a higher number of PMI compared with standard LGE sequence (19/54 vs. 15/54) with an increased sharpness of PMI (84.2 vs. 53.3%). The CNR was higher between infarcted myocardium and blood (77.9 ± 60 vs. 19.3 ± 16, P < 0.001) and between PMI and blood (69.4 ± 51 vs. 39.4 ± 26, respectively, P = 0.0157). CONCLUSIONS: Our data indicate that in patients with myocardial infarction, LGE CMR imaging using a short TI may be more sensitive than standard LGE imaging for the detection of PMI.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".