Effects of denosumab on bone histomorphometry: The FREEDOM and STAND studies
Bibliographic record
Abstract
Denosumab, a human monoclonal antibody against RANKL, reversibly inhibits osteoclast-mediated bone resorption and has been developed for use in osteoporosis. Its effects on bone histomorphometry have not been described previously. Iliac crest bone biopsies were collected at 24 and/or 36 months from osteoporotic postmenopausal women in the FREEDOM study (45 women receiving placebo and 47 denosumab) and at 12 months from postmenopausal women previously treated with alendronate in the STAND study (21 continuing alendronate and 15 changed to denosumab at trial entry). Qualitative histologic evaluation of biopsies was unremarkable. In the FREEDOM study, median eroded surface was reduced by more than 80% and osteoclasts were absent from more than 50% of biopsies in the denosumab group. Double labeling in trabecular bone was observed in 94% of placebo bones and in 19% of those treated with denosumab. Median bone-formation rate was reduced by 97%. Among denosumab-treated subjects, those with double labels and those with absent labels had similar levels of biochemical markers of bone turnover. In the STAND trial, indices of bone turnover tended to be lower in the denosumab group than in the alendronate group. Double labeling in trabecular bone was seen in 20% of the denosumab biopsies and in 90% of the alendronate samples. Denosumab markedly reduces bone turnover and also reduces fracture numbers. Longer follow-up is necessary to determine how long such low turnover is safe.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".