Myositis in systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVES: Myositis is an infrequent feature of SLE and may often be overlooked. We aimed to estimate the incidence of myositis in SLE, and to determine demographic and clinical factors associated with it. METHODS: Within our lupus cohort, we identified potential myositis cases using the SLICC Damage Index for muscle atrophy or weakness, the SLEDAI-2K item for myositis, and annually measured serum creatinine kinase. Cases were confirmed through chart review. We performed descriptive analyses of prevalent myositis cases as of January 2000. From that point onward, we studies patients without myositis to determine risk of incident myositis, using cohort analyses adjusted for demographic variables (age, sex, race/ethnicity). RESULTS: As of January 2000, there were 5 prevalent myositis cases in our SLE cohort. Among 560 SLE patients with a study visit from January 2000 onward, with no history of myositis at baseline, 5 new cases (4 females, 1 male) were identified over an average follow-up of 8.5 years (incidence 1.05 cases per 1000 person-years). There was a higher proportion of Caucasians in the non-myositis group versus myositis group, with a trend for fewer females in the myositis cases. Arthritis, Raynaud's phenomenon, and anti-Smith antibodies were common pre-existing features, occurring in all incident myositis cases. In Cox regression analyses adjusting for age, race/ethnicity and sex, non-Caucasian patients had a markedly increased risk of developing myositis. CONCLUSION: We found a low incidence of myositis in our SLE cohort. A cluster of variables, particularly non-Caucasian race/ethnicity, arthritis, Raynaud's phenomenon, and anti-Smith antibodies were associated with risk of developing myositis in SLE. These variables may aid clinicians in identifying SLE patients at highest risk for this important complication.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".