Hepatic Steatosis and Disease Activity in Subjects with Psoriatic Arthritis Receiving Tumor Necrosis Factor-α Blockers
Bibliographic record
Abstract
OBJECTIVE: Little is known about tumor necrosis factor-α (TNF-α) blockers, disease activity, and liver steatosis (hepatic steatosis; HS) in subjects with psoriatic arthritis (PsA). We prospectively evaluated changes in HS during treatment with TNF-α blockers. METHODS: In 48 patients with PsA who had evidence of HS before the beginning of TNF-α blocker treatment, an ultrasound followup examination was performed after a 12-month treatment period with TNF-α blockers. All subjects were stratified according to minimal disease activity (MDA) or not (n-MDA), during treatment with TNF-α blockers. Changes in grade of HS were evaluated in parallel in 42 controls with HS and without PsA. RESULTS: At baseline, no significant difference in HS score was found between PsA subjects and controls (HS scores 1.46 ± 0.65 vs 1.62 ± 0.66, respectively; p = 0.249). At 12-month followup, a worsening HS score was found in 20 (41.7%) patients with PsA and in 6 (14.3%) controls (p = 0.005). Overall, the grade of HS worsening was higher in patients with PsA (0.37 ± 0.70) than in controls (0.09 ± 0.43; p = 0.028). A significantly lower prevalence of worsening HS was found among patients with PsA with MDA, compared with n-MDA subjects (16.7% vs 66.7%, respectively; p = 0.001). Laboratory measures of liver function behaved similarly. The risk of worsening HS in patients with PsA who had MDA was similar to that in controls (HR 1.20, 95% CI 0.34-4.33, p = 0.77), and higher in patients who did not have MDA (HR 4.46, 95% CI 1.73-11.47, p = 0.001, regression analysis). CONCLUSION: Compared with patients with MDA, those with active disease after 12-month treatment with TNF-α blockers exhibited significantly higher incidence of worsening liver steatosis.
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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 | 0.000 | 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 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".