Monitoring of Osteonecrosis in Systemic Lupus Erythematosus: A Systematic Review and Metaanalysis
Bibliographic record
Abstract
OBJECTIVE: Nontraumatic osteonecrosis (ON) is a well-recognized complication causing disability and affecting quality of life in patients with systemic lupus erythematosus (SLE). The aim of this study was to identify the risk factors for ON, and to identify the minimal investigation(s) needed to optimally monitor the risk of ON in patients with SLE. METHODS: A systematic review was conducted using MEDLINE and EMBASE. These databases were searched up to January 2016 using the Medical Subject Heading (MeSH) terms "Osteonecrosis," "Systemic lupus erythematosus," and synonymous text words. Randomized controlled trials, case control, cohort, and cross-sectional studies were included. Risk factors for ON in patients with SLE were compiled. The quality of each study was assessed using the Newcastle-Ottawa scale for nonrandomized studies. The quality of evidence of each risk factor was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation method. RESULTS: Of the 545 references yielded, 50 met inclusion criteria. Corticosteroid (CS) use may be strongly associated with ON in patients with SLE. Other clinical variables were moderately associated, including hypertension, serositis, renal disease, vasculitis, arthritis, and central nervous system disease. However, the evidence was low to very low in quality. CONCLUSION: Based on the best evidence available, CS use may be strongly associated with ON in patients with SLE. Results of this review were considered in the development of recommendations for the diagnosis and monitoring of patients with SLE in Canada and will guide clinicians in their assessment of these patients.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.024 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".