Longitudinal Association Between Trabecular Bone Loss and Disease Activity in Axial Spondyloarthritis: A 4-year Prospective Study
Bibliographic record
Abstract
OBJECTIVE: To investigate whether trabecular bone loss is longitudinally associated with disease activity measures in patientswith axial spondyloarthritis (axSpA). METHODS: Data from patients enrolled in the Incheon Saint Mary's axSpA prospective observational cohort were evaluated. Trabecular bone loss was assessed using the trabecular bone score (TBS). The relationship between TBS and disease activity measures [Ankylosing Spondylitis Disease Activity Score (ASDAS), Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP)] was investigated using generalized estimating equation (GEE) models. RESULTS: Four-year followup data from 240 patients (80% males, mean age 37 ± 12 yrs) were evaluated. At baseline, higher disease activity according to ASDAS-ESR and ASDAS-CRP showed a trend toward lower TBS (p = 0.003 and p = 0.016, respectively). Univariate GEE analyses showed a significant association between TBS and disease activity measures over time, with the exception of BASDAI. Univariate analysis showed a longitudinal association between TBS and age, smoking, and spinal structural damage. In multivariate GEE analysis, ASDAS-ESR, ASDAS-CRP, ESR, and CRP were longitudinally associated with TBS after adjustment for confounding factors. ASDAS scores and inflammatory markers were longitudinally associated with TBS in patients with ankylosing spondylitis (AS; 79%), but not in patients with nonradiographic axSpA (nr-axSpA). BASDAI scores showed no relationship with TBS in either the AS or nr-axSpA groups. CONCLUSION: Trabecular bone loss in patients with axSpA, assessed using the TBS, showed a longitudinal association with ASDAS scores and inflammatory markers.
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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 | 0.001 |
| 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) | 0.001 | 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".