A cross sectional study assessing steatotic liver disease in patients with systemic lupus erythematosus
Bibliographic record
Abstract
Patients with immune-mediated inflammatory diseases are prone to steatotic liver disease (SLD), which has been observed in patients with psoriasis and hidradenitis suppurativa. We aimed to assess whether systemic lupus erythematosus (SLE) was associated with SLD and to define factors associated with SLD in SLE. This was a cross-sectional study, we included 106 consecutive patients with SLE who were seen in the rheumatology clinic between June 2021 and March 2022 and we chose two sex-paired controls for each SLE. All the participants underwent FibroScan and anthropometric assessments. SLD was defined as a controlled attenuation parameter ≥ 275dB/m. Prevalence of SLD was lower in patients with SLE (21.7% vs 41.5%, p < 0.001). Patients with SLE and SLD had a lower frequency of hydroxychloroquine use (65% vs 84%, p = 0.04), and higher C3 levels [123mg/dl (IQR 102-136) vs 99mg/dl (IQR 78-121), p = 0.004]. Factors associated with SLD in SLE were body mass index (BMI), waist circumference, glucose, and C3; hydroxychloroquine use was a protective factor. On univariate analysis, SLE was associated with a reduced risk of SLD (OR 0.39, 95%CI 0.23-0.67); however, after adjusting for age, BMI, waist, glucose, triglycerides, high-density cholesterol, low-density cholesterol, leukocytes, and hydroxychloroquine, it was no longer associated (OR 0.43, 95%CI 0.10-1.91). In conclusion, the prevalence of SLD in patients with SLE was not higher than that in the general population, and SLE was not associated with SLD. The factors associated with SLD were anthropometric data, glucose, hydroxychloroquine, and C3 levels.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".