Meta‐Analysis of Tumor Necrosis Factor Inhibitors and Glucocorticoids on Bone Density in Rheumatoid Arthritis and Ankylosing Spondylitis Trials
Bibliographic record
Abstract
OBJECTIVE: To examine the impact of antirheumatic drugs on bone mineral density (BMD) in rheumatoid arthritis (RA), ankylosing spondylitis (AS), psoriatic arthritis (PsA), and psoriasis using a systematic review. METHODS: Electronic databases were systematically searched for randomized controlled trials. Studies were grouped based on disease, treatment, and site of BMD measurement. Change in BMD (ΔBMD) from baseline to end of study was recorded. Standardized mean difference (SMD) of ΔBMD between treatment and controls was standardized for meta-analyses and 95% confidence intervals (95% CIs) were calculated. RESULTS: Treatment effects on BMD were not the primary outcomes of the trials. Thirteen studies were eligible (11 RA, 2 AS, 0 PsA, and 0 psoriasis). For RA, significantly less hand bone loss was seen with tumor necrosis factor inhibitors (TNFi; SMD ΔBMD 0.33 [95% CI 0.13, 0.53], P = 0.001, I(2) = 0%) and glucocorticoids (SMD ΔBMD 0.51 [95% CI 0.20, 0.81], P = 0.001, I(2) = 0%). TNFi had no significant effect on lumbar spine and hip BMD. Glucocorticoids were associated with a negative effect on lumbar spine (SMD ΔBMD -0.30 [95% CI -0.55, -0.04], P = 0.02, I(2) = 52%), but not hip BMD. For AS, a significant increase in BMD was seen with TNFi at the lumbar spine (SMD ΔBMD 0.96 [95% CI 0.64, 1.27], P < 0.001, I(2) = 16%) and hip (SMD ΔBMD 0.38 [95% CI 0.13, 0.62], P = 0.003, I(2) = 0%). Data were insufficient to perform meta-analyses in PsA and psoriasis or for other antirheumatic drugs. CONCLUSION: In RA, TNFi and glucocorticoids appeared to attenuate hand bone loss. TNFi did not impact lumbar spine and hip BMD and glucocorticoids had negative effects on lumbar spine and no effect on hip BMD. In AS, TNFi was associated with improved lumbar spine and hip BMD.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.043 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".