Denosumab in Men Receiving Androgen-Deprivation Therapy for Prostate Cancer
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Androgen-deprivation therapy is well-established for treating prostate cancer but is associated with bone loss and an increased risk of fracture. We investigated the effects of denosumab, a fully human monoclonal antibody against receptor activator of nuclear factor-kappaB ligand, on bone mineral density and fractures in men receiving androgen-deprivation therapy for nonmetastatic prostate cancer. METHODS: In this double-blind, multicenter study, we randomly assigned patients to receive denosumab at a dose of 60 mg subcutaneously every 6 months or placebo (734 patients in each group). The primary end point was percent change in bone mineral density at the lumbar spine at 24 months. Key secondary end points included percent change in bone mineral densities at the femoral neck and total hip at 24 months and at all three sites at 36 months, as well as incidence of new vertebral fractures. RESULTS: At 24 months, bone mineral density of the lumbar spine had increased by 5.6% in the denosumab group as compared with a loss of 1.0% in the placebo group (P<0.001); significant differences between the two groups were seen at as early as 1 month and sustained through 36 months. Denosumab therapy was also associated with significant increases in bone mineral density at the total hip, femoral neck, and distal third of the radius at all time points. Patients who received denosumab had a decreased incidence of new vertebral fractures at 36 months (1.5%, vs. 3.9% with placebo) (relative risk, 0.38; 95% confidence interval, 0.19 to 0.78; P=0.006). Rates of adverse events were similar between the two groups. CONCLUSIONS: Denosumab was associated with increased bone mineral density at all sites and a reduction in the incidence of new vertebral fractures among men receiving androgen-deprivation therapy for nonmetastatic prostate cancer. (ClinicalTrials.gov number, NCT00089674.)
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 it