Human non-contrast T1 values and correlation with histology in diffuse fibrosis
Bibliographic record
Abstract
BACKGROUND: Aortic stenosis (AS) leads to diffuse fibrosis in the myocardium, which is linked to adverse outcome. Myocardial T1 values change with tissue composition. OBJECTIVE: To test the hypothesis that our recently developed non-contrast cardiac magnetic resonance (CMR) T1 mapping sequence could identify myocardial fibrosis without contrast agent. DESIGN, SETTING AND PATIENTS: A prospective CMR non-contrast T1 mapping study of 109 patients with moderate and severe AS and 33 age- and gender-matched controls. METHODS: CMR at 1.5 T, including non-contrast T1 mapping using a shortened modified Look-Locker inversion recovery sequence, was carried out. Biopsy samples for histological assessment of collagen volume fraction (CVF%) were obtained in 19 patients undergoing aortic valve replacement. RESULTS: There was a significant correlation between T1 values and CVF% (r=0.65, p=0.002). Mean T1 values were significantly longer in all groups with severe AS (972 ± 33 ms in severe asymptomatic, 1014 ± 38 ms in severe symptomatic) than in normal controls (944 ± 16 ms) (p<0.05). The strongest associations with T1 values were for aortic valve area (r=-0.40, p=0.001) and left ventricular mass index (LVMI) (r=0.36, p=0.008), and these were the only independent predictors on multivariate analysis. CONCLUSIONS: Non-contrast T1 values are increased in patients with severe AS and further increase in symptomatic compared with asymptomatic patients. T1 values lengthened with greater LVMI and correlated with the degree of biopsy-quantified fibrosis. This may provide a useful clinical assessment of diffuse myocardial fibrosis in the future.
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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 | 0.002 |
| 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".