Prevalence and Factors Associated With Osteoporosis and Bone Mineral Density Testing in Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: To determine bone mineral density (BMD) in psoriatic arthritis (PsA) patients, factors associated with undergoing BMD testing, and the effect of PsA clinical activity on BMD. METHODS: Patients attending the University of Toronto PsA Clinic with BMD testing results from cohort inception to January 2019 were included. Descriptive statistics summarized lumbar spine, femoral neck, and total hip T scores. Cox proportional hazards regression identified predictors for BMD testing. Logistic regression analysis determined odds of having normal (T score -1.0 or more) versus osteoporotic-range BMD (T score -2.5 or less). A multistate model determined factors associated with BMD state changes over time. RESULTS: Of the 1,479 patients, 214 had BMD tests performed. The mean ± SD T scores at the lumbar spine, femoral neck, and total hip were -0.30 ± 0.32, -1.10 ± 1.04, and -0.45 ± 0.42, respectively. Osteopenia and osteoporosis occurred in 45.27% and 12.94% of patients. Increasing age, menopause, elevated acute-phase reactants, and biologics, methotrexate, and systemic glucocorticoids use were associated with a higher chance of undergoing BMD testing. Increased body mass index (BMI) and biologics use were associated with a lower chance of having osteoporotic-range BMD test results. In multistate analysis, polyarthritis may portend lower BMD results over time, although this did not achieve statistical significance due to low patient numbers. CONCLUSION: The prevalence of osteopenia and osteoporosis in the PsA cohort was similar to that of the general population. Clinicians are using osteoporosis risk factors and PsA disease severity markers to select patients for BMD testing. Polyarticular disease may portend worse BMD test results. Biologic use and increased BMI appear to have a protective effect.
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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 | 0.005 |
| 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 | 0.001 | 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".