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Record W4252377846 · doi:10.5489/cuaj.117

Management of skeletal-related events in patients with advanced prostate cancer and bone metastases: Incorporating new agents into clinical practice

2012· article· en· W4252377846 on OpenAlexafffundvenueabout
Alan So, Joseph L. Chin, Neil Fleshner, Fred Saad

Bibliographic record

VenueCanadian Urological Association Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsUniversité de MontréalUniversity of TorontoLondon Health Sciences CentreUniversity Health NetworkUniversity of British Columbia
FundersAmgen CanadaAmgen
KeywordsDenosumabZoledronic acidMedicineProstate cancerOncologyInternal medicineCancerOsteoporosis

Abstract

fetched live from OpenAlex

Skeletal-related events (SREs) are a common complication of bone metastases, and have serious negative consequences for patients with castrate-resistant prostate cancer (CRPC). SREs can lead to severe pain, increased risk of death, increased health care costs and reduced quality of life. Until recently, zoledronic acid has been the sole standard of care for the prevention of SREs in men with CRPC with bone metastases. Denosumab, a receptor activator of nuclear factor kappa-B ligand (RANK-L) inhibitor, has been recently approved for use in Canada for this indication, thus presenting another option for these patients. Denosumab was shown to be superior to zoledronic acid in delaying the time to first or subsequent SREs in CRPC patients with bone metastases. This review discusses current and previous trials examining agents designed to prevent SREs in men with CRPC and bone metastases. It also discusses the practical aspects of administering a bone-targeted therapy, including choosing a bone-targeted therapy, monitoring at the onset and during therapy, switching from one therapy to another, and assessing potential complications.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

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

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 head0.016
GPT teacher head0.316
Teacher spread0.299 · 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 teacher head, 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

Citations66
Published2012
Admission routes4
Has abstractyes

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