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Record W3174854556 · doi:10.1002/jbmr.4409

Romosozumab improves lumbar spine bone mass and bone strength parameters relative to alendronate in postmenopausal women: results from the Active-Controlled Fracture Study in Postmenopausal Women With Osteoporosis at High Risk (ARCH) trial

2021· article· en· W3174854556 on OpenAlexaff
Jacques P. Brown, Klaus Engelke, Tony M. Keaveny, Arkadi Chines, Roland Chapurlat, A. Joseph Foldes, Xavier Nogués, Roberto Civitelli, Tobias De Villiers, Fabio Massari, Cristiano A. F. Zerbini, Zhenxun Wang, Mary Oates, Christopher Recknor, Cesar Libanati

Bibliographic record

VenueJournal of Bone and Mineral Research · 2021
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversité LavalCentre hospitalier universitaire de Québec
FundersAmgen
KeywordsMedicinePostmenopausal womenOsteoporosisLumbar spineBone massFRAXBone mineralInternal medicineOsteoporotic fractureSurgery

Abstract

fetched live from OpenAlex

The Active-Controlled Fracture Study in Postmenopausal Women With Osteoporosis at High Risk (ARCH) trial (NCT01631214; https://clinicaltrials.gov/ct2/show/NCT01631214) showed that romosozumab for 1 year followed by alendronate led to larger areal bone mineral density (aBMD) gains and superior fracture risk reduction versus alendronate alone. aBMD correlates with bone strength but does not capture all determinants of bone strength that might be differentially affected by various osteoporosis therapeutic agents. We therefore used quantitative computed tomography (QCT) and finite element analysis (FEA) to assess changes in lumbar spine volumetric bone mineral density (vBMD), bone volume, bone mineral content (BMC), and bone strength with romosozumab versus alendronate in a subset of ARCH patients. In ARCH, 4093 postmenopausal women with severe osteoporosis received monthly romosozumab 210 mg sc or weekly oral alendronate 70 mg for 12 months, followed by open-label weekly oral alendronate 70 mg for ≥12 months. Of these, 90 (49 romosozumab, 41 alendronate) enrolled in the QCT/FEA imaging substudy. QCT scans at baseline and at months 6, 12, and 24 were assessed to determine changes in integral (total), cortical, and trabecular lumbar spine vBMD and corresponding bone strength by FEA. Additional outcomes assessed include changes in aBMD, bone volume, and BMC. Romosozumab caused greater gains in lumbar spine integral, cortical, and trabecular vBMD and BMC than alendronate at months 6 and 12, with the greater gains maintained upon transition to alendronate through month 24. These improvements were accompanied by significantly greater increases in FEA bone strength (p < 0.001 at all time points). Most newly formed bone was accrued in the cortical compartment, with romosozumab showing larger absolute BMC gains than alendronate (p < 0.001 at all time points). In conclusion, romosozumab significantly improved bone mass and bone strength parameters at the lumbar spine compared with alendronate. These results are consistent with greater vertebral fracture risk reduction observed with romosozumab versus alendronate in ARCH and provide insights into structural determinants of this differential treatment effect. © 2021 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.321
Teacher spread0.300 · 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 designRandomized trial
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

Citations73
Published2021
Admission routes1
Has abstractyes

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