The lack of association between cumulative MTX dose and liver fibrosis in PsA: a cohort study
Bibliographic record
Abstract
OBJECTIVES: Evidence for the association between MTX use and liver fibrosis in PsA remains inconclusive. We aimed to explore the frequency of liver fibrosis in PsA and identify associated factors including cumulative MTX dose and metabolic features. METHODS: We analysed data from a prospective observational PsA cohort. We calculated the aspartate aminotransferase to platelet ratio index (APRI), a non-invasive marker commonly used to assess fibrosis in patients with liver diseases. A cut-off of >0.7 was used to denote liver fibrosis. We conducted univariable and multivariable generalized estimating equations (GEEs) analysis to assess the impact of cumulative MTX dose and metabolic factors on liver fibrosis, adjusting for confounders such as demographic characteristics, comorbidities and medications. RESULTS: One thousand three hundred and fourteen patients were included in the study, with a mean age of 44.4 (S.D. 13.1) at baseline (clinic entry). Of these, 375 (28.5%) patients were receiving MTX at baseline, while 763 (58.1%) had ever received the medication. Forty-four (3.3%) patients fulfilled the definition of liver fibrosis per APRI at baseline, while 154 (11.7%) developed liver fibrosis during follow-up at a median of 5.6 [IQR: 2.0-11.1] years from baseline. In the multivariable model, adjusted for confounders, cumulative MTX dose was not independently associated with liver fibrosis (OR 0.99, 95% CI 0.98-1.01). However, higher BMI (OR 1.03, 95% CI 1.00-1.05) and diabetes mellitus (OR 5.03, 95% CI 2.19-11.56) showed a significant association. CONCLUSION: Metabolic factors, rather than the cumulative MTX dose, are associated with liver fibrosis in PsA.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".