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Record W2653957511 · doi:10.1097/spc.0000000000000280

The role of bone-targeted therapies for prostate cancer in 2017

2017· review· en· W2653957511 on OpenAlexaff
Samer L. Traboulsi, Fred Saad

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2017
Typereview
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersAstellas PharmaAmgen
KeywordsMedicineDenosumabAbiraterone acetateProstate cancerEnzalutamideZoledronic acidOncologyRandomized controlled trialClinical trialInternal medicineCancerOsteoporosisAndrogen deprivation therapyAndrogen receptor

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Bone-targeted agents (BTAs), such as zoledronic acid and denosumab, delay the occurrence of skeletal-related events (SREs) in metastatic prostate cancer (PCa) patients. Recently, several agents, such as abiraterone acetate, enzalutamide and radium-223, were approved for the treatment of metastatic castration-resistant PCa (mCRPC). These agents resulted in improved overall survival (OS), pain control and had positive effects on bone health. Combining BTAs to the newly approved agents demonstrates additional benefits that warrant a review of available evidence looking at appropriate combination therapies and timing of BTAs for optimizing the management of advanced and metastatic PCa. RECENT FINDINGS: Post-hoc analyses of randomized trials demonstrated some benefits from combination therapy, such as increased OS when denosumab was used concurrently with radium-223 and when BTAs were used with abiraterone acetate. BTAs were not beneficial for the prevention of bone metastases. SUMMARY: There is a suggestion of synergy or additive effects between BTAs and new agents approved for the treatment of metastatic PCa, resulting in potential clinical benefits. Therefore, prospective randomized studies evaluating the safety and benefits of combination therapies to address gaps in the literature are needed to optimize treatment of mCRPC.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.939
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.197
GPT teacher head0.501
Teacher spread0.304 · 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 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

Citations5
Published2017
Admission routes1
Has abstractyes

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