Increase in Bone Density in Patients with Spondyloarthritis During Anti-Tumor Necrosis Factor Therapy: 6-year Followup Study
Bibliographic record
Abstract
OBJECTIVE: To assess the effects on bone mineral density (BMD) of prolonged anti-tumor necrosis factor (anti-TNF) therapy in patients with spondyloarthritis (SpA); to compare the BMD changes to those observed in SpA patients not treated with anti-TNF; and to identify the predictors of these changes. METHODS: Fifty-nine patients with SpA according to the European Spondylarthropathy Study Group criteria who were treated with anti-TNF therapy for at least 4 years were included. Thirty-four patients with SpA from an international longitudinal observational study (OASIS cohort) were used as a control group. Lumbar spine and hip BMD were measured by dual-energy x-ray absorptiometry at baseline, after 1 year, and after at least 4 years. RESULTS: Over an average 6.5 years' followup, the increase in BMD was 11.8% (± 12.8%) at the lumbar spine (p < 0.0001) and 3.6% (± 9.3%) at the great trochanter (p = 0.0001) in patients treated with anti-TNF. At the lumbar spine, the increase was similar in patients with and those without syndesmophytes. BMD changes were significantly higher in the anti-TNF group than in the control group at lumbar spine (p < 0.0001), at femoral neck (p = 0.002), and at trochanter (p = 0.011), but not at total hip (p = 0.062). Multivariate analysis showed that the predictors of lumbar spine BMD changes in the total population were the use of anti-TNF (p < 0.0001) and, in the anti-TNF therapy group, the 1-year lumbar spine BMD change (p = 0.007). CONCLUSION: This study shows that prolonged anti-TNF therapy increases lumbar spine and trochanter BMD. This effect should be taken into account before introducing antiosteoporotic treatment in these patients.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".