Bone mineral density in rheumatoid arthritis patients 1 year after adalimumab therapy: arrest of bone loss
Bibliographic record
Abstract
OBJECTIVE: To explore the effects of anti-tumour necrosis factor (TNF)alpha antibody therapy on bone mineral density (BMD) of the lumbar spine and femur neck in patients with rheumatoid arthritis (RA). METHODS: A total of 50 patients with active RA (DAS28> or =3.2) who started adalimumab (40 mg subcutaneously/2 weeks) were included in an open label prospective study. All patients used stable methotrexate and were allowed to use prednisone (< or =10 mg/day). The BMD of the lumbar spine and femur neck was measured before and 1 year after start of treatment. RESULTS: Disease activity at baseline (28-joint Disease Activity Score (DAS28)) and disease duration were inversely correlated with femoral neck BMD and lumbar spine BMD (p<0.05). Mean BMD of lumbar spine and femur neck remained unchanged after 1 year of adalimumab therapy (+0.3% and +0.3%, respectively). Of interest, a beneficial effect of prednisone on change in femur neck BMD was observed with a relative increase with prednisone use (+2.5%) compared to no concomitant prednisone use (-0.7%), (p = 0.015). CONCLUSION: In contrast to the progressive bone loss observed after conventional disease-modifying antirheumatic drug therapy, TNF blockade may result in an arrest of general bone loss. Consistent with previous observations, the data also suggest that the net effect of low-dose corticosteroids on BMD in RA may be beneficial, possibly resulting from their anti-inflammatory effects.
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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 | 0.001 |
| 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 | 0.001 | 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".