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Record W2044287047 · doi:10.3747/co.19.1281

Targeting the Androgen Receptor in the Management of Castration-Resistant Prostate Cancer: Rationale, Progress, and Future Directions

2012· article· en· W2044287047 on OpenAlexaffvenue
Raya Leibowitz‐Amit, Anthony M. Joshua

Bibliographic record

VenueCurrent Oncology · 2012
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsEnzalutamideProstate cancerMedicineAbiraterone acetateAndrogen receptorDiseaseClinical trialAbirateroneHormonal therapyCancerBioinformaticsOncologyCancer researchPharmacologyAndrogen deprivation therapyInternal medicineBiology

Abstract

fetched live from OpenAlex

Since the year 2000, tremendous progress has been made in the understanding of castration-resistant prostate cancer (crpc), a disease state now recognized to retain androgen receptor (ar)-dependency in most cases. That understanding led to the rational design of novel therapeutic agents targeting hormonal pathways in metastatic crpc. Two new drugs-the CYP17 inhibitor abiraterone acetate and the potent ar antagonist enzalutamide-were recently shown to prolong overall survival after chemotherapy treatment in patients with metastatic disease, with the former agent also demonstrating impressive activity in the pre-chemotherapy setting. Other new drugs targeting the ar-as well as drugs targeting heat shock proteins that protect cytoplasmic ar from degradation-are currently undergoing clinical development.This review briefly describes the molecular mechanisms underlying castration resistance and hormonal dependence in prostate tumours and summarizes the current ongoing and completed clinical trials that are targeting hormonal pathways in metastatic crpc. Potential mechanisms of resistance to these novel hormonal agents are reviewed. Finally, future research directions, including questions about drug sequencing and combination, are discussed.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.852
Threshold uncertainty score0.155

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.064
GPT teacher head0.403
Teacher spread0.339 · 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
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

Citations42
Published2012
Admission routes2
Has abstractyes

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