Wall strain index ratio as a biomarker for hemorrhagic myocardial infarction mechanical complications
Bibliographic record
Abstract
statistical significance (100% v 87%; p=0.5).For subendocardial infarcts (≤25% transmural), the sensitivity of BN-DE dropped to 81% (p=0.03) and accuracy to 85% (p< 0.01).Figure 2 shows typical images in two patients with MI showing that contrast between blood pool and infarction is consistently high for FIDDLE but can be low for BN-DE.Not surprisingly, the MI-to-blood-pool CNR was higher for FIDDLE (57.8±37.6)compared to BN-DE (7.9 ±19.7; p< 0.0001).Figure 3 demonstrates that blood pool homogeneity can occasionally be poor for BN-DE.This is likely due to the short inversion time required for BN-DE imaging which may accentuate small differences in apparent T1 due to flow effects, B1 inhomogeneity, etc.Conclusion: FIDDLE provides improved diagnostic performance compared to BN-DE particularly in subendocardial infarcts.Gray blood techniques such as BN-DE may not detect infarcts when the T1 of blood and infarct are similar.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".