Hepatic steatosis as measured by the computed attenuation parameter predicts fibrosis in long-term methotrexate use
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/m2 (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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".