Prevalence and Risk Factors for Liver Biochemical Abnormalities in Canadian Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of abnormal liver enzymes in patients with systemic lupus erythematosus (SLE) and whether further investigations were done, and the differences in SLE-related and/or metabolic factors in patients with and without liver biochemical abnormalities. METHOD: Patients from the University of Toronto Lupus Clinic who met at least 4 of the American College of Rheumatology classification criteria for SLE and had 1.5 times the upper limit for aspartate transaminase or alanine transaminase on 2 consecutive visits within a 2-year period were matched with controls for age, sex, and SLE duration. Demographic, clinical, and laboratory data were extracted at the time of the first appearance of liver enzyme abnormality for the cases and at the reference point for the controls. RESULTS: From the 1533 patients reviewed, 134 (8.7%) met the inclusion criteria. Thirty of these patients were evaluated by a hepatologist, 75 had imaging studies (41 were done specifically for liver investigation), and 13 had liver biopsies. Results based on these investigations showed 31 fatty livers, 35 cases of drug-induced hepatotoxicity, 10 autoimmune etiologies, and 3 cases of viral hepatitis. Compared to controls, cases were higher in body mass index, anti-dsDNA antibody, prevalence of hypertension, antiphospholipid syndrome, and use of immunosuppressive medication, especially azathioprine and methotrexate; they were lower in IgM. CONCLUSION: Metabolic abnormalities such as obesity and hypertension and hepatotoxic effects of medication used to treat SLE may contribute more than SLE-related factors to liver biochemical abnormalities in patients with SLE.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".