T<sub>1</sub> independent, T<sub>2</sub><sup>*</sup> corrected chemical shift based fat–water separation with multi‐peak fat spectral modeling is an accurate and precise measure of hepatic steatosis
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: To determine the precision and accuracy of hepatic fat-fraction measured with a chemical shift-based MRI fat-water separation method, using single-voxel MR spectroscopy (MRS) as a reference standard. MATERIALS AND METHODS: In 42 patients, two repeated measurements were made using a T(1) -independent, T 2*-corrected chemical shift-based fat-water separation method with multi-peak spectral modeling of fat, and T(2) -corrected single voxel MR spectroscopy. Precision was assessed through calculation of Bland-Altman plots and concordance correlation intervals. Accuracy was assessed through linear regression between MRI and MRS. Sensitivity and specificity of MRI fat-fractions for diagnosis of steatosis using MRS as a reference standard were also calculated. RESULTS: Statistical analysis demonstrated excellent precision of MRI and MRS fat-fractions, indicated by 95% confidence intervals (units of absolute percent) of [-2.66%,2.64%] for single MRI ROI measurements, [-0.81%,0.80%] for averaged MRI ROI, and [-2.70%,2.87%] for single-voxel MRS. Linear regression between MRI and MRS indicated that the MRI method is highly accurate. Sensitivity and specificity for detection of steatosis using averaged MRI ROI were 100% and 94%, respectively. The relationship between hepatic fat-fraction and body mass index was examined. CONCLUSION: Fat-fraction measured with T(1) -independent T 2*-corrected MRI and multi-peak spectral modeling of fat is a highly precise and accurate method of quantifying hepatic steatosis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it