Trabecular Bone Score in Female Patients with Systemic Sclerosis: Comparison with Rheumatoid Arthritis and Influence of Glucocorticoid Exposure
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis (SSc) is associated with an increased risk of osteoporosis and fractures. To date, the etiology of bone loss in SSc is unclear. Trabecular bone score (TBS) provides an indirect measurement of bone microarchitecture, independent of areal bone mineral density (aBMD). The aims were to assess bone involvement in SSc using TBS in comparison with a "high-risk" population with rheumatoid arthritis (RA) and controls, and to investigate the determinants of a low TBS. METHODS: This was a cross-sectional study of 65 women with SSc, 138 age-matched female patients with RA, and 227 age-matched female controls. Spine and hip aBMD were assessed using dual-energy X-ray absorptiometry. TBS was calculated from the anteroposterior image of the spine aBMD. RESULTS: TBS was significantly lower in SSc compared to controls (p < 0.0001) and did not differ from RA (p = 0.128), despite lower cumulative and daily glucocorticoid (GC) dose (p < 0.0001). Further, patients with SSc receiving GC ≥ 5 mg/day had a significantly lower TBS than those receiving GC < 5 mg/day (p = 0.001). Multivariate analysis revealed that a low TBS was independently associated with daily GC dose (OR 5.6, 95% CI 1.7-19.2) and a T score ≤ -2.5 SD (OR 5.0, 95% CI 1.5-7.0) in SSc. No association between GC and TBS was found in RA. CONCLUSION: Our results support the development of a combined approach using both TBS and aBMD for the assessment of bone microarchitecture in inflammatory rheumatic diseases. Our study showed that SSc-related bone involvement is characterized by an impairment in bone quality in addition to reduced bone quantity, and highlights that TBS can identify the negative effect of GC on bone microarchitecture.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".