Systemic Lupus Erythematosus, Osteoporosis, and Fractures
Bibliographic record
Abstract
Osteoporosis is a common yet less-recognized complication of systemic lupus erythematosus (SLE) that has been brought to our attention by Zhu, et al ’s article in this issue of The Journal 1. Recent studies, however, have highlighted the high prevalence of fractures in a relatively young population of women suffering from SLE1,2,3,4,5,6,7. There have been many studies reporting different results. This might be explained by the complex relationship between SLE, complications of the disease itself or its treatment, duration of disease, bone loss, and fractures. Indeed the systemic inflammation associated with SLE and the resultant end-organ damage may all play a role1,2,3. Combine this with the traditional risk factors for fracture and it is easy to understand why the study of osteoporosis and fractures in SLE is complex. Confounders include active inflammation and treatment of inflammation with glucocorticoids1,2,3, menopausal status, and disease-associated complications such as renal bone disease and neuropsychiatric disease3. Other non-SLE risk factors including age, medications such as selective serotonin receptor inhibitors (SSRI) and possibly proton pump inhibitors (PPI) may all confound the relationship between SLE and fractures. The effect of treatment with bone-active drugs to prevent bone loss and fractures must also be taken into consideration in studies of SLE. ### Inflammation There is … Address correspondence to Dr. Adachi, St. Joseph’s Hospital, 501-25 Charlton Ave. East, Hamilton, Ontario L8N 1Y2, Canada. E-mail: jd.adachi{at}sympatico.ca
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".