Bone disease in systemic sclerosis: outcomes and associations.
Bibliographic record
Abstract
OBJECTIVES: The relationship between systemic sclerosis (SSc) and low bone mineral density (BMD) is poorly understood. The aim of this study is to improve our understanding of low bone density in SSc and its potential consequences. METHODS: Fifty consecutive unselected SSc patients were approached. Demographics, disease manifestations, BMD (lumbar spine and femoral neck) were collected at baseline and occurrence of fracture and death were collected over 2 years. The 10-year risk of osteoporotic fracture was estimated using the fracture risk assessment tool (FRAX) v2.0 with the Canadian population reference. Fisher's Exact and Student's t-tests were used to evaluate differences between patients with and without low BMD. Logistic regression was used for multivariate analysis. RESULTS: Forty-five patients had complete BMD data. Twenty-eight patients (62%) had low BMD, of those 10 (36%) had osteoporosis. There was no difference in age, sex, or disease duration between both groups. Low BMD was associated with non-Caucasian race (57% vs. 18%, p=0.01), postmenopausal status (83% vs. 47%, p<0.01), low body mass index (24.5 vs. 26.2, p=0.05). The mean 10-year risk of developing a major osteoporotic fracture and a femoral neck fracture was higher in the low BMD group (10.2% vs. 4.8%, p=0.12) and (4.1% vs. 0.5%, p = 0.16) respectively. Fourteen percent (4/28) of SSc patients with low BMD had a fracture, compared to 6% (1/17) SSc patients without low BMD. Fracture-related mortality did not occur in any patients. CONCLUSIONS: Low BMD and fracture are frequently seen in SSc patients. A number of clinically relevant factors are associated with low BMD. Further research is needed to evaluate these factors and the role of bone-specific treatments in SSc.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".