A 24-month subgroup analysis of the effect of denosumab on bone mineral density in women with breast cancer undergoing aromatase inhibitor therapy.
Bibliographic record
Abstract
Abstract Abstract #2106 Background: Accelerated bone loss and fracture risk are expected consequences of adjuvant aromatase inhibitor (AI) therapy. We previously showed that denosumab, a fully human monoclonal antibody that inhibits RANK ligand (RANKL), significantly increased bone mineral density (BMD) at the lumbar spine and at all measured skeletal sites at 12 months compared with placebo in women with breast cancer undergoing adjuvant AI therapy (Ellis et al, 2007 SABCS). In this analysis, we assessed covariates that may influence treatment effects on BMD at the lumbar spine, total hip, femoral neck, and 1/3 radius at 24 months. Methods: Adult patients (pts) with hormone receptor-positive breast cancer, who had evidence of low bone mass and were receiving adjuvant AI therapy, were enrolled in this randomized, double-blind, placebo-controlled, phase 3 study. Pts were stratified according to length of previous AI therapy (≤ 6 vs > 6 months) and randomly assigned to receive, together with calcium and vitamin D, placebo (n=125) or denosumab 60 mg (n=127) subcutaneously every 6 months for 4 doses. Subgroup analysis was conducted using analysis of covariance and adjusted for treatment, stratification factor, baseline BMD value, densitometer type, and baseline BMD value-by-densitometer-type interaction. Results: At 24 months, greater increases in BMD were seen at all measured skeletal sites (both trabecular and cortical bone) for denosumab compared with placebo, regardless of the subgroup (table). Adverse events (AEs) occurred at a similar rate in both groups (91% denosumab, 90% placebo). Conclusion: In pts with breast cancer undergoing adjuvant AI therapy, twice-yearly denosumab treatment showed consistent increases in BMD across the skeleton at 24 months compared with placebo, regardless of subgroups. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 2106.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".