Hepatosteatosis and Atherosclerotic Plaque at Coronary CT Angiography
Why this work is in the frame
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Bibliographic record
Abstract
Purpose To assess the association between nonalcoholic fatty liver disease (NAFLD) and quantitative atherosclerotic plaque at CT. Materials and Methods In this post hoc analysis of the prospective Scottish Computed Tomography of the HEART trial (November 2010 to September 2014), hepatosteatosis and coronary artery calcium score were measured at noncontrast CT. Presence of stenoses, visually assessed high-risk plaque, and quantitative plaque burden were assessed at coronary CT angiography. Multivariable models were constructed to assess the impact of hepatosteatosis and cardiovascular risk factors on coronary artery disease. Results Images from 1726 participants (mean age, 58 years ± 9 [SD]; 974 men) were included. Participants with hepatosteatosis (155 of 1726, 9%) had a higher body mass index, more hypertension and diabetes mellitus, and higher cardiovascular risk scores (P < .001 for all) compared with those without hepatosteatosis. They had increased coronary artery calcium scores (median, 43 Agatston units [AU] [interquartile range, 0–273] vs 19 AU [0–225], P = .046), more nonobstructive disease (48% vs 37%, P = .02), and higher low-attenuation plaque burden (5.11% [0–7.16] vs 4.07% [0–6.84], P = .04). However, these associations were not independent of cardiovascular risk factors. Over a median of 4.7 years, there was no evidence of a difference in myocardial infarction between those with and without hepatosteatosis (1.9% vs 2.4%, P = .92). Conclusion Hepatosteatosis at CT was associated with an increased prevalence of coronary artery disease at CT, but this was not independent of the presence of cardiovascular risk factors. Keywords: CT, Cardiac, Nonalcoholic Fatty Liver Disease, Coronary Artery Disease, Hepatosteatosis, Plaque Quantification Clinical trial registration no. NCT01149590 Supplemental material is available for this article. © RSNA, 2022 See also commentary by Abohashem and Blankstein in this issue.
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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) | 0.000 | 0.000 |
| 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 it