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Record W4367052200 · doi:10.5489/cuaj.8381

Adjuvant therapy for renal cell carcinoma

2023· article· en· W4367052200 on OpenAlexaffvenueabout
Aly‐Khan A. Lalani, Anil Kapoor, Naveen S. Basappa, Bimal Bhindi, Georg A. Bjarnason, Dominick Bossé, Rodney H. Breau, Christina M. Canil, Luisa M. Cardenas, Vincent Castonguay, Claudia Chávez‐Muñoz, William Chu, Shaan Dudani, Jeffrey Graham, Daniel Y.C. Heng, Christian Kollmannsberger, Jean‐Baptiste Lattouf, Scott C. Morgan, M. Neil Reaume, Patrick O. Richard, Anand Swaminath, Simon Tanguay, Lori Wood, Luke T. Lavallée

Bibliographic record

VenueCanadian Urological Association Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsDalhousie UniversityUniversité de MontréalWilliam Osler Health SystemCentre hospitalier universitaire de QuébecMcGill UniversityUniversity of OttawaCentre Hospitalier Universitaire de SherbrookeUniversity of British ColumbiaCentre hospitalier de l'Université LavalOttawa HospitalHealth Sciences CentreMcMaster UniversityHôtel-Dieu de QuébecUniversity of CalgaryUniversity of AlbertaUniversity of ManitobaJuravinski Cancer CentreSt. Joseph’s Healthcare HamiltonSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineRenal cell carcinomaKidney cancerContext (archaeology)ReferralPembrolizumabAdjuvantFamily medicineInternal medicineAdjuvant therapySystemic therapyRadiation oncologistOncologyRandomized controlled trialRegimenIntensive care medicineRadiation therapyCancerBreast cancerImmunotherapy

Abstract

fetched live from OpenAlex

INTRODUCTION: Several recent randomized trials evaluated the impact of adjuvant immune checkpoint inhibitor (ICI)-based therapy on post-surgical outcomes in renal cell carcinoma (RCC), with disparate results. The objective of this consensus statement is to provide data-driven guidance regarding the use of ICIs after complete resection of clear-cell RCC in a Canadian context. METHODS: An expert panel of genitourinary medical oncologists, urologic oncologists, and radiation oncologists with expertise in RCC management was convened in a dedicated session during the 2022 Canadian Kidney Cancer Forum in Toronto, Canada. Topic statements on the management of patients after surgery for RCC, including counselling, risk stratification, indications for medical oncology referral, appropriate followup, eligibility and management for adjuvant ICIs, as well as treatment options for patients with recurrence who received adjuvant immunotherapy, were discussed. Participants were asked to vote if they agreed or disagreed with each statement. Consensus was achieved if greater than 75% of participants agreed with the topic statement. RESULTS: A total of 22 RCC experts voted on 14 statements. Consensus was achieved on all topic statements. The panel felt patients with clear-cell RCC at increased risk of recurrence after surgery, as per the Keynote-564 group definitions, should be counselled about recurrence risk by a urologist, should be informed about the potential role of adjuvant ICI systemic therapy, and be offered referral to discuss risks and benefits with a medical oncologist. The panel felt that one year of pembrolizumab is currently the only regimen that should be considered if adjuvant therapy is selected. Panelists emphasized current opinions are based on disease-free survival given the available results. Significant uncertainty regarding the benefit and harms of adjuvant therapy remains, primarily due to a lack of consistent benefit observed across similar trials of adjuvant ICI-based therapies and immature overall survival (OS) data. CONCLUSIONS: This consensus document provides guidance from Canadian RCC experts regarding the potential role of ICI-based adjuvant systemic therapy after surgery. This rapidly evolving field requires frequent evidence-based re-evaluation.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.028
GPT teacher head0.244
Teacher spread0.216 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations10
Published2023
Admission routes3
Has abstractyes

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