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Record W4386387581 · doi:10.1007/s11523-023-00987-1

Summary of Research: Adjuvant Nivolumab Plus Ipilimumab Versus Placebo for Localized Renal Cell Carcinoma After Nephrectomy (CheckMate 914): A Double-Blind, Randomized, Phase 3 Trial

2023· article· en· W4386387581 on OpenAlexaff
Robert J. Motzer, Paul Russo, Viktor Grünwald, Yoshihiko Tomita, Bogdan Żurawski, Omi Parikh, Sebastiano Buti, Philippe Barthélémy, Jeffrey C. Goh, Dingwei Ye, Alejo Lingua, Jean‐Baptiste Lattouf, Laurence Albigès, Saby George, Brian Shuch, Jeffrey A. Sosman, Michael Staehler, Sergio Vázquez Estévez, Burçin Şimşek, Julia Spiridigliozzi, Aleksander Chudnovsky, Axel Bex

Bibliographic record

VenueTargeted Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersNational Cancer InstituteOno PharmaceuticalMemorial Sloan-Kettering Cancer CenterBristol-Myers Squibb
KeywordsNivolumabMedicineIpilimumabRenal cell carcinomaPlaceboNephrectomyInternal medicineAdjuvantOncologyAdjuvant therapyKidney cancerClinical trialCancerSurgeryImmunotherapyKidneyPathologyAlternative medicine

Abstract

fetched live from OpenAlex

This is a summary of a research article reporting Part A of the CheckMate 914 study (NCT03138512; EudraCT 2016-004502-34). Following surgery to remove renal cell carcinoma (RCC), people with a high risk of the cancer returning received nivolumab plus ipilimumab (adjuvant therapy) or placebo to see if this risk was reduced. The results of this study showed that the risk of RCC returning or death was not changed with adjuvant nivolumab plus ipilimumab treatment compared with placebo. In addition, people treated with nivolumab plus ipilimumab had more side effects compared with people treated with placebo (89% versus 57%).

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)0.004
metaresearch head score (Gemma)0.004
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 score0.019
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0190.003

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 head0.129
GPT teacher head0.406
Teacher spread0.277 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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