Hepatic steatosis as measured by the computed attenuation parameter predicts fibrosis in long-term methotrexate use
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: To determine predictors of hepatic steatosis by the computed attenuation parameter (CAP) and fibrosis via transient elastography (TE) in persons on methotrexate (MTX) therapy with rheumatologic and dermatologic diseases. METHODS: A single-centred retrospective cohort study was performed. Patients on >6 months of MTX for a rheumatologic or dermatologic disease who had undergone TE from January 2015 to September 2019 were included. Multivariate analysis was performed to determine predictors of steatosis and fibrosis. RESULTS: A total of 172 patients on methotrexate were included. Psoriasis was the most frequent diagnosis ( n = 55), followed by rheumatoid arthritis ( n = 45) and psoriatic arthritis ( n = 34). Steatosis (CAP ≥245 dB/m) was present in 69.8% of patients. Multivariate regression analysis revealed that diabetes mellitus (OR 10.47, 95% CI 1.42–75.35), hypertension (OR 5.15, 95% CI 1.75–15.38), and BMI ≥30 kg/m 2 (OR 16.47, 95% CI 5.56–45.56) were predictors of steatosis (CAP ≥245 dB/m). Predictors of moderate to severe fibrosis (Metavir ≥F2 = TE ≥8.0 kPa) by multivariate regression analysis included moderate to severe steatosis (CAP ≥270 dB/m) (OR 8.36, 95% CI 1.88–37.14), diabetes mellitus (OR 2.85, 95% CI 1.09–7.48), hypertension (OR 5.4, 95% CI 2.23–13.00), dyslipidemia (OR 3.71, 95% CI 1.50–9.18), and moderate alcohol use (OR 3.06, 95% CI 1.2–7.49). CONCLUSIONS: In patients on MTX for rheumatologic and dermatologic diseases, hepatic steatosis as measured by CAP was common and moderate to severe steatosis predicted moderate to severe fibrosis.
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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) | 0.001 | 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 it