MétaCan
Menu
Back to cohort
Record W4361245262 · doi:10.1093/oncolo/oyad008

Niraparib with Abiraterone Acetate and Prednisone for Metastatic Castration-Resistant Prostate Cancer: Phase II QUEST Study Results

2023· article· en· W4361245262 on OpenAlexaff
Kim N., Neil Fleshner, Vincenzo Emanuele Chiurì, Siska Van Bruwaene, Jason Hafron, Douglas G. McNeel, Peter De Porre, Raymond Scott Maul, Mahesh Daksh, Xiaogang Zhong, Gary Mason, Ronald Tutrone

Bibliographic record

VenueThe Oncologist · 2023
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of British Columbia
FundersJanssen BiotechJanssen Research and DevelopmentAmgenAstellas Foundation for Research on Metabolic DisordersAstellas PharmaUroGen PharmaDendreon PharmaceuticalsProgenics PharmaceuticalsSanofiPfizerMyriad GeneticsAstraZenecaEli Lilly and Company
KeywordsAbiraterone acetateMedicineProstate cancerPrednisoneAndrogen receptorAbirateroneAndrogen deprivation therapyInternal medicineOncologyCancer researchPARP1Testosterone (patch)PharmacologyCancerPoly ADP ribose polymerasePolymeraseGeneBiologyGenetics

Abstract

fetched live from OpenAlex

Niraparib (NIRA) is a highly selective inhibitor of poly (adenosine diphosphate-ribose) polymerase, PARP1 and PARP2, which play a role in DNA repair. The phase II QUEST study evaluated NIRA combinations in patients with metastatic castration-resistant prostate cancer who were positive for homologous recombination repair gene alterations and had progressed on 1 prior line of novel androgen receptor-targeted therapy. Results from the combination of NIRA with abiraterone acetate plus prednisone, which disrupts androgen axis signaling through inhibition of CYP17, showed promising efficacy and a manageable safety profile in this patient population.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.090
GPT teacher head0.418
Teacher spread0.328 · 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 designNon-randomized trial
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

Citations17
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueThe OncologistSame topicPARP inhibition in cancer therapyFrench-language works237,207