Osteonecrosis in SLE: prevalence, patterns, outcomes and predictors
Bibliographic record
Abstract
Objective Osteonecrosis is a serious comorbidity in patients with systemic lupus erythematosus. The aims of this study were to describe the prevalence of symptomatic osteonecrosis, determine the pattern of joint involvement, identify the outcomes and investigate predictive factors in a large cohort of patients with systemic lupus erythematosus followed prospectively. Methods At the Toronto Lupus Clinic patients have been followed prospectively according to a standard protocol since 1970. Osteonecrosis is recorded if patients are symptomatic and is confirmed by imaging. The site of osteonecrosis is recorded and whether or not surgery was performed. For determination of prevalence, pattern and outcome of osteonecrosis a longitudinal cohort design was performed. For the predictive factors, only patients with incident osteonecrosis were included and were matched for gender, year of entry to clinic (within 5 years), year of birth (within 5 years) and disease duration (within 3 years) with systemic lupus erythematosus patients without osteonecrosis. Results Of 1729 patients with systemic lupus erythematosus registered in the database, 234 (13.5%) developed symptomatic osteonecrosis in 581 sites. Hips and knees were most commonly affected and 47% of the patients had multiple sites involved. More than half of the joints involved at first occurrence of osteonecrosis had surgery. Univariate analysis identified black race, damage, elevated cholesterol and glucocorticosteroids as predictive factors, but glucocorticosteroids remained as the primary predictor for the development of osteonecrosis on multivariable analysis. Conclusion Despite advancements in the assessment and treatment of systemic lupus erythematosus, symptomatic osteonecrosis continues to be a significant comorbidity. Strategies to minimize glucocorticosteroid use are necessary to prevent this serious 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 | 0.001 | 0.001 |
| 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".