MétaCan
Menu
Back to cohort
Record W3042347895 · doi:10.1002/cncr.33033

Nivolumab versus everolimus in patients with advanced renal cell carcinoma: Updated results with long‐term follow‐up of the randomized, open‐label, phase 3 CheckMate 025 trial

2020· article· en· W3042347895 on OpenAlexaff
Robert J. Motzer, Bernard Escudier, Saby George, Hans J. Hammers, Sandhya Srinivas, Scott S. Tykodi, Jeffrey A. Sosman, Elizabeth R. Plimack, Giuseppe Procopio, David F. McDermott, Daniel Castellano, Toni K. Choueiri, Frede Donskov, Howard Gurney, Stéphane Oudard, Martin Eduardo Richardet, Katriina Peltola, Ajjai Alva, Michael A. Carducci, John Wagstaff, Christine Chevreau, Satoshi Fukasawa, Yoshihiko Tomita, Thomas Gauler, Christian Kollmannsberger, Fabio A.B. Schutz, James Larkin, David Cella, M. Brent McHenry, Shruti Shally Saggi, Nizar M. Tannir

Bibliographic record

VenueCancer · 2020
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsBC Cancer Agency
FundersNational Cancer InstituteNational Institutes of HealthBristol-Myers Squibb
KeywordsMedicineNivolumabEverolimusOpen labelRandomized controlled trialRenal cell carcinomaInternal medicineOncologyImmunotherapyCancer

Abstract

fetched live from OpenAlex

BACKGROUND: CheckMate 025 has shown superior efficacy for nivolumab over everolimus in patients with advanced renal cell carcinoma (aRCC) along with improved safety and tolerability. This analysis assesses the long-term clinical benefits of nivolumab versus everolimus. METHODS: The randomized, open-label, phase 3 CheckMate 025 trial (NCT01668784) included patients with clear cell aRCC previously treated with 1 or 2 antiangiogenic regimens. Patients were randomized to nivolumab (3 mg/kg every 2 weeks) or everolimus (10 mg once a day) until progression or unacceptable toxicity. The primary endpoint was overall survival (OS). The secondary endpoints were the confirmed objective response rate (ORR), progression-free survival (PFS), safety, and health-related quality of life (HRQOL). RESULTS: Eight hundred twenty-one patients were randomized to nivolumab (n = 410) or everolimus (n = 411); 803 patients were treated (406 with nivolumab and 397 with everolimus). With a minimum follow-up of 64 months (median, 72 months), nivolumab maintained an OS benefit in comparison with everolimus (median, 25.8 months [95% CI, 22.2-29.8 months] vs 19.7 months [95% CI, 17.6-22.1 months]; hazard ratio [HR], 0.73; 95% CI, 0.62-0.85) with 5-year OS probabilities of 26% and 18%, respectively. ORR was higher with nivolumab (94 of 410 [23%] vs 17 of 411 [4%]; P < .001). PFS also favored nivolumab (HR, 0.84; 95% CI, 0.72-0.99; P = .0331). The most common treatment-related adverse events of any grade were fatigue (34.7%) and pruritus (15.5%) with nivolumab and fatigue (34.5%) and stomatitis (29.5%) with everolimus. HRQOL improved from baseline with nivolumab but remained the same or deteriorated with everolimus. CONCLUSIONS: The superior efficacy of nivolumab over everolimus is maintained after extended follow-up with no new safety signals, and this supports the long-term benefits of nivolumab monotherapy in patients with previously treated aRCC. LAY SUMMARY: CheckMate 025 compared the effects of nivolumab (a novel immunotherapy) with those of everolimus (an older standard-of-care therapy) for the treatment of advanced kidney cancer in patients who had progressed on antiangiogenic therapy. After 5 years of study, nivolumab continues to be better than everolimus in extending the lives of patients, providing a long-lasting response to treatment, and improving quality of life with a manageable safety profile. The results demonstrate that the clinical benefits of nivolumab versus everolimus in previously treated patients with advanced kidney cancer continue in the long term.

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)0.003
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 score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
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)0.0020.000

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 head0.037
GPT teacher head0.297
Teacher spread0.260 · 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

Citations337
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCancerSame topicRenal cell carcinoma treatmentFrench-language works237,207