Using FibroScan to Assess for the Development of Liver Fibrosis in Patients With Arthritis on Methotrexate: A Single-center Experience
Bibliographic record
Abstract
Objective. Methotrexate (MTX) is often the primary medication to treat various rheumatic diseases (RDs) because of its low cost and its demonstrated efficacy in controlling disease activity. However, a concern has been the potential for hepatic fibrosis associated with long-term MTX usage. This study investigated the association between cumulative MTX intake and development of liver fibrosis by utilizing noninvasive transient elastography (FibroScan). Methods. All patients with inflammatory arthritis treated with MTX were offered screening with FibroScan. A certified technician measured liver stiffness after patients adhered to a fast. Relevant clinical information was obtained by patient survey and medical records review. The population was divided into quartiles based on participants’ cumulative dosage of MTX. Results. Five hundred twenty patients with RD were included in this study. The prevalence of stages F3 or F4 liver fibrosis was 13.3% in the control group and 12.7% in the entire sample. Compared with subgroup 1 (control with cumulative MTX exposure of ≤ 499 mg), MTX subgroups 2 to 4 were not significantly correlated with higher FibroScan scores (P= 0.82, 0.59, and 0.18, respectively). In multivariable linear regression analysis, statistically significant factors for liver stiffness were BMI, waist circumference, male sex, and age. Conclusion. No significant correlation between the cumulative MTX dosage and liver stiffness, even at high MTX doses, was observed. The analyses showed significant correlations between the FibroScan score and BMI. These findings were reassuring in that current rheumatology practice appears to be safe and effective in screening for liver fibrosis in patients on long-term low-dose MTX therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".