Anti-tumor Necrosis Factor Therapy Increased Spine and Femoral Neck Bone Mineral Density of Patients with Active Ankylosing Spondylitis with Low Bone Mineral Density
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of anti-tumor necrosis factor (TNF) therapy on bone mineral density (BMD) in patients with active ankylosing spondylitis (AS) with low BMD. METHODS: Eighty-nine patients with active AS with low BMD were randomly divided into either a study group or a control group. The study group received etanercept (50 mg/week) or adalimumab (40 mg/2 week) subcutaneously for 1 year. BMD of lumbar spine and femoral neck was measured by dual energy X-ray absorptiometry, and bone turnover markers serum C telopeptide of type-I collagen (sCTX) and serum procollagen type-I N propeptide (PINP) were detected by ELISA at baseline and at end of study. RESULTS: After 1 year, compared with baseline, there was a significant increase in spine and femoral neck BMD by a mean ± SD of 14.9% ± 15.6% (p < 0.0001) and 4.7% ± 7.9% (p < 0.0001) in the study group. In the control group, there was a significant decrease in spine and femoral neck BMD by a mean ± SD of -8.6% ± 9.7% (p < 0.0001) and -9.8% ± 11.5% (p < 0.0001). Compared with baseline, sCTX was significantly decreased in the study group (-40% at 1 yr, p < 0.0001), but bone-specific alkaline phosphatase and PINP increased (45.6%, p < 0.0001 and 30.8%, p < 0.0001, respectively). CONCLUSION: In patients with active AS with low BMD, the spine and femoral neck BMD increased after anti-TNF therapy for 1 year, and it was accompanied by a significant decrease in bone resorption markers and an increase in bone formation 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".