A pooled data analysis on the use of intermittent cyclical etidronate therapy for the prevention and treatment of corticosteroid induced bone loss
Bibliographic record
Abstract
OBJECTIVE: To conduct a pooled data analysis in a group of patients defined by sex, menopausal status, and underlying disease in order to examine the effect of intermittent cyclical etidronate in the prevention and treatment of corticosteroid induced osteoporosis. METHODS: We selected 5 randomized, placebo controlled studies that examined the efficacy of intermittent cyclical etidronate therapy in which the raw data were available for analysis. Three were prevention studies and 2 treatment studies. The primary outcome was the difference between treatment groups in the percentage change from baseline in lumbar spine bone density. Secondary outcomes included the difference between treatment groups in the percentage change from baseline in femoral neck and trochanter bone density, and vertebral fracture rates. RESULTS: Results are separately pooled for the prevention and treatment studies. The prevention studies had significant mean differences (95% CI) between groups in mean percentage change from baseline in lumbar spine, femoral neck, and trochanter bone density of 3.7 (2.6 to 4.7), 1.7 (0.4 to 2.9), and 2.8% (1.3 to 4.2) after one year of treatment, in favor of the etidronate group. The treatment studies displayed a mean difference between groups in mean percentage change from baseline in lumbar spine bone density of 4.8 (2.7 to 6.9) and 5.4% (2.5 to 8.4) after one and 2 years of therapy. In the prevention studies, a reduced fracture incidence was observed in the etidronate group compared with the placebo group (relative risk 0.50; CI 0.21 to 1.19). CONCLUSION: Etidronate therapy was effective in preventing bone loss in the prevention studies and in preventing or slightly increasing bone mass in the treatment studies. A fracture benefit was observed in postmenopausal women treated with etidronate in the prevention studies.
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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 | 0.042 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".