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Record W2067721174 · doi:10.3390/jcm2030067

Bone-Targeted Agents for the Management of Breast Cancer Patients with Bone Metastases

2013· review· en· W2067721174 on OpenAlexaff
Demetrios Simos, Christina Addison, Iryna Kuchuk, Brian Hutton, Sasha Mazzarello, Mark Clemons

Bibliographic record

VenueJournal of Clinical Medicine · 2013
Typereview
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineBreast cancerRANKLCancerDenosumabDiseaseBone metastasisTargeted therapyQuality of life (healthcare)OncologyBone painBone diseasePalliative careInternal medicineBioinformaticsOsteoporosisActivator (genetics)Receptor

Abstract

fetched live from OpenAlex

Despite advances in adjuvant therapy for breast cancer, bone remains the most common site of recurrence. The goal of therapy for these patients is palliative and focused on maximizing the duration and quality of their life, while concurrently minimizing any disease or treatment-related complications. Bone metastases predispose patients to reduced survival, pain, impaired quality of life and the development of skeletal-related events. With an increased understanding of the pathophysiology of bone metastasis, effective treatments for their management have evolved and are now in widespread clinical use. This article will discuss the pathogenesis of bone metastases and review the key clinical evidence for the efficacy and safety of currently available systemic bone-targeted therapies in breast cancer patients with an emphasis on bisphosphonates and the receptor activator of nuclear factor kappa B ligand (RANKL) inhibitors. We will also discuss novel strategies and therapies currently in development.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.160
GPT teacher head0.492
Teacher spread0.332 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations10
Published2013
Admission routes1
Has abstractyes

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