Prevalence and risk factors for liver fibrosis detected by transient elastography or shear wave elastography in inflammatory arthritis: a systematic review.
Bibliographic record
Abstract
OBJECTIVES: Emerging technologies for monitoring subclinical liver fibrosis include transient elastography (TE) and shear wave elastography (SWE). A systematic review was conducted to assess the prevalence and report on predictors of liver fibrosis as detected by these technologies in inflammatory arthritis (IA) patients, including rheumatoid arthritis, spondyloarthritis and juvenile idiopathic arthritis. METHODS: MEDLINE, EMBASE and Web of Science were searched from inception to 06/27/2016 using search terms for IA or DMARDs and TE/SWE. Studies reporting on prevalence and/or risk factors for liver fibrosis as detected by TE/SWE were included. A meta-analysis was not conducted due to study heterogeneity. RESULTS: Seven cross-sectional and three case-control studies were included. The cut-off values to define liver fibrosis ranged from 5.3-8.6 kPa. The prevalence of liver fibrosis in RA detected by TE/SWE ranged from 3-23%, with higher prevalence found in studies using a 5.3kPa cut-off. In two studies fibrosis was reported in 16-17% of PsA patients with no JIA studies identified. Obesity was the most consistently reported independent predictor of fibrosis in three studies. Liver function tests (LFTs) were found to independently predict increased liver stiffness in one study, while cumulative dose of either methotrexate or leflunomide were predictors in two studies. CONCLUSIONS: Methotrexate or leflunomide cumulative dose was not consistently reported as an independent predictor of liver fibrosis; whereas, obesity was more consistently identified. Of note, LFTs did not consistently predict elevated TE/SWE measures. Further studies are needed to evaluate the prevalence and predictors of liver fibrosis and to explore the utility of using TE/SWE in IA patients.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".