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Record W2323619027 · doi:10.1158/0008-5472.sabcs-2106

A 24-month subgroup analysis of the effect of denosumab on bone mineral density in women with breast cancer undergoing aromatase inhibitor therapy.

2009· article· en· W2323619027 on OpenAlexaff
GK Ellis, HG Bone, R. T. Chlebowski, Devchand Paul, Silvana Spadafora, Musei Fan, D Kim

Bibliographic record

VenueCancer Research · 2009
Typearticle
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsEssar Steel Algoma (Canada)
Fundersnot available
KeywordsMedicineDenosumabPlaceboBreast cancerBone mineralFemoral neckInternal medicineAromatase inhibitorOsteoporosisUrologyBone remodelingSubgroup analysisAdjuvant therapyOncologyEndocrinologyCancerAromataseMeta-analysisPathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.027
GPT teacher head0.367
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2009
Admission routes1
Has abstractyes

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