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Record W4318046437 · doi:10.1016/j.eururo.2023.01.001

Outcomes for International Metastatic Renal Cell Carcinoma Database Consortium Prognostic Groups in Contemporary First-line Combination Therapies for Metastatic Renal Cell Carcinoma

2023· article· en· W4318046437 on OpenAlexaff
Matthew Scott Ernst, Vishal Navani, J. Connor Wells, Frede Donskov, Naveen S. Basappa, Chris Labaki, Sumanta K. Pal, Luís Meza, Lori Wood, D. Scott Ernst, Bernadett Szabados, Rana R. McKay, Francis Parnis, Cristina Suárez, Takeshi Yuasa, Aly‐Khan A. Lalani, Ajjai Alva, Georg A. Bjarnason, Toni K. Choueiri, Daniel Y.C. Heng

Bibliographic record

VenueEuropean Urology · 2023
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsUniversity of TorontoSunnybrook Health Science CentreJuravinski Cancer CentreOttawa Regional Cancer FoundationMcMaster UniversityQueen Elizabeth II Health Sciences CentreUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineRenal cell carcinomaOncologyInternal medicineCarcinoma

Abstract

fetched live from OpenAlex

BACKGROUND: The combination of immuno-oncology (IO) agents ipilimumab and nivolumab (IPI-NIVO) and vascular endothelial growth factor targeted therapies (VEGF-TT) combined with IO (IO-VEGF) are current standard of care first-line treatments for metastatic renal cell carcinoma (mRCC). OBJECTIVE: To establish real-world clinical benchmarks for IO combination therapies based on the International mRCC Database Consortium (IMDC) criteria. DESIGN, SETTING, AND PARTICIPANTS: Patients with mRCC who received first-line IPI-NIVO, IO-VEGF, or VEGF-TT from 2002 to 2021 were identified using the IMDC database and stratified according to IMDC risk groups. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Overall survival (OS), time to next treatment (TTNT), and treatment duration (TD) were calculated using the Kaplan-Meier method and compared between IMDC risk groups within each treatment cohort by the log-rank test. The overall response rate (ORR) was calculated by physician assessment of the best overall response. The primary outcome was OS at 18 mo. RESULTS AND LIMITATIONS: In total, 728 patients received IPI-NIVO, 282 IO-VEGF, and 7163 VEGF-TT. The median follow-up times for patients remaining alive were 14.3 mo for IPI-NIVO, 14.9 mo IO-VEGF, and 34.4 mo for VEGF-TT. OS at 18 mo for favorable, intermediate, and poor risk was, respectively, 90%, 78%, and 50% for those receiving IPI-NIVO; 93%, 83%, and 74% for IO-VEGF; and 84%, 64%, and 28% for VEGF-TT. ORRs in favorable-, intermediate-, and poor-risk groups were 41.3%, 40.6%, and 33.0% for those receiving IPI-NIVO; 60.3%, 56.8%, and 40.9% for IO-VEGF; and 39.3%, 33.5%, and 20.9% for VEGF-TT, respectively. The IMDC model stratified patients into statistically distinct risk groups for the three endpoints of OS, TTNT, and TD within each treatment cohort. Limitations of this study were the retrospective design and short follow-up. CONCLUSIONS: This study demonstrated that the IMDC model continues to risk stratify patients with mRCC treated with contemporary first-line IO combination therapies and provided real-world survival benchmarks. PATIENT SUMMARY: The International Metastatic Renal Cell Carcinoma Database Consortium model continues to stratify patients with metastatic renal cell carcinoma receiving modern combination treatments in the real-world setting.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.073
GPT teacher head0.298
Teacher spread0.225 · 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 designObservational
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

Citations68
Published2023
Admission routes1
Has abstractyes

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