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Record W4205173478 · doi:10.2217/fon-2021-1096

A Phase III, Randomized, Open-Label Study (CONTACT-02) of Cabozantinib Plus Atezolizumab Versus Second Novel Hormone Therapy in Patients with Metastatic Castration-Resistant Prostate Cancer

2022· article· en· W4205173478 on OpenAlexaff
Neeraj Agarwal, Arun Azad, Joan Carles, Simon Chowdhury, Bradley A. McGregor, Axel S. Merseburger, Stéphane Oudard, Fred Saad, Andrey Soares, Fawzi Benzaghou, Yannick Kerloëguen, Akiko Kimura, Nehal Mohamed, Ashok Panneerselvam, Fong Wang, Sumanta K. Pal

Bibliographic record

VenueFuture Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersExelixis
KeywordsCabozantinibMedicineAtezolizumabProstate cancerEnzalutamideOncologyInternal medicineClinical trialCancerNivolumabImmunotherapyAndrogen receptor

Abstract

fetched live from OpenAlex

Cabozantinib inhibits multiple receptor tyrosine kinases, including the TAM kinase family, and may enhance response to immune checkpoint inhibitors. One cohort of the ongoing phase Ib COSMIC-021 study (NCT03170960) evaluating cabozantinib plus the PD-L1 inhibitor atezolizumab in men with metastatic castration-resistant prostate cancer (mCRPC) that has progressed in soft tissue on/after enzalutamide and/or abiraterone treatment for metastatic disease has shown promising efficacy. Here, we describe the rationale and design of a phase III trial of cabozantinib plus atezolizumab versus a second novel hormone therapy (NHT) in patients who have previously received an NHT for mCRPC, metastatic castration-sensitive PC or nonmetastatic CRPC and have measurable visceral disease and/or extrapelvic adenopathy – a population with a significant unmet need for treatment options. Trial Registration Clinical Trial Registration: NCT04446117 (ClinicalTrials.gov) Registered on 24 June 2020.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0100.002

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 head0.058
GPT teacher head0.391
Teacher spread0.334 · 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 designRandomized 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

Citations36
Published2022
Admission routes1
Has abstractyes

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