Osteoporosis screening, prevention, and treatment in systemic lupus erythematosus: application of the systemic lupus erythematosus quality indicators
Bibliographic record
Abstract
OBJECTIVE: Osteoporosis and fragility fractures are associated with significant morbidity for patients with systemic lupus erythematosus (SLE). New quality indicators (QIs) for SLE advise bone mineral density testing, calcium and vitamin D use, and antiresorptive or anabolic treatment for specific subgroups of patients receiving high-dose steroids. METHODS: Subjects were participants in the University of California, San Francisco Lupus Outcomes Study, an ongoing longitudinal study of patients with physician-confirmed SLE, in 2007-2008. Patients responded to an annual telephone survey and were queried regarding demographic, clinical, and other health care-related variables. Multiple logistic regression was used to predict receipt of care per the QIs described above. RESULTS: One hundred twenty-seven patients met the criteria for the formal definitions of the denominators for QI I (screening) and QI II (calcium and vitamin D); 91 met the formal criteria for QI III (treatment). The proportions of patients receiving care consistent with the QIs were 74%, 58%, and 56% for QIs I, II, and III, respectively. In a sensitivity analysis of all steroid users (n = 427 for QI I and II and n = 224 for QI III), rates were slightly lower. Predictors of receiving care varied by QI and by denominator; however, female sex, older age, white race, and longer disease duration were associated with higher-quality care. CONCLUSION: Bone health-related care in this community-based cohort of SLE patients is suboptimal. Quality improvement efforts should address osteoporosis prevention and care among all SLE patients, especially those receiving high-dose, prolonged steroids.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".