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Record W168572226 · doi:10.17925/eoh.2012.08.3.148

Weighing Bone-targeted Treatment Options for Patients with Solid Tumours and Skeletal Complications from Metastatic Disease

2012· article· en· W168572226 on OpenAlexaff
Lesley Fallowfield, Roger von Moos, Luís Costa, Alison Stopeck, Mark Clemons, Noel W. Clarke, Ada Braun, Karen Chung

Bibliographic record

VenueEuropean Oncology & Haematology · 2012
Typearticle
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineQuality of life (healthcare)DiseaseClinical trialIntensive care medicineOncologyPhysical therapyInternal medicineNursing

Abstract

fetched live from OpenAlex

The complications of metastatic bone disease (MBD) in advanced cancer, especially skeletal-related events (SREs), are a significant cause of morbidity that can seriously impair the quality of patients’ lives. Treatments that prevent SREs, reduce or delay the onset of pain and preserve function and activities of daily living are central to good patient care. In this article, we discuss results from clinical trials that show the relative benefits and harms of different bone-targeted agents, which may be given orally, intravenously or subcutaneously. These data, when considered alongside various patient characteristics, can provide oncologists with better opportunities to individualise care. Optimal management with treatments that enhance efficacy and adherence mean that clinicians can improve the outlook for their patients with MBD, who may consequently experience fewer SREs and less pain and enjoy a better overall quality of life.

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)0.006
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.029
GPT teacher head0.318
Teacher spread0.289 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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