Quantitative texture features as objective metrics of enhancement heterogeneity in hypertrophic cardiomyopathy
Bibliographic record
Abstract
Background Hypertrophic cardiomyopathy (HCM) results in myocardial disarray, hypertrophy and fibrosis. Late gadolinium enhanced MRI (LGE) can assess the presence and extent of fibrosis, which is associated with the development of arrhythmias and sudden cardiac death. However, enhancement may not always be present or only sparsely distributed. Thus, one of the challenges is how best to describe heterogeneous LGE patterns in an objective fashion that informs clinical decision making. Quantitative texture features may provide clinicians with an objective means of describing the heterogeneity of LGE patterns in HCM. We hypothesized that hypertrophied segments would exhibit greater grey-level heterogeneity than both (a) non-hypertrophied segments in HCM patients, and (b) healthy volunteers.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".