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Record W4394869936 · doi:10.1093/oncolo/oyae072

Impact of smoking status on clinical outcomes in patients with metastatic renal cell carcinoma treated with first-line immune checkpoint inhibitor-based regimens

2024· article· en· W4394869936 on OpenAlexaff
Eddy Saad, Georges Gebrael, Karl Semaan, Marc Eid, Renée Maria Saliby, Chris Labaki, Nicolas Sayegh, J. Connor Wells, Kosuke Takemura, Matthew Scott Ernst, Audreylie Lemelin, Naveen S. Basappa, Lori Wood, Thomas Powles, D. Scott Ernst, Aly‐Khan A. Lalani, Neeraj Agarwal, Wanling Xie, Daniel Y.C. Heng, Toni K. Choueiri

Bibliographic record

VenueThe Oncologist · 2024
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsMcMaster UniversityJuravinski Cancer CentreDalhousie UniversityWestern UniversityBaker Hughes (Canada)Queen Elizabeth II Health Sciences CentreUniversity of AlbertaUniversity of CalgaryBC Cancer Agency
Fundersnot available
KeywordsMedicineRenal cell carcinomaInternal medicineConfoundingLogistic regressionProportional hazards modelUnivariate analysisOncologyKidney cancerMultivariate analysis

Abstract

fetched live from OpenAlex

BACKGROUND: Current tobacco smoking is independently associated with decreased overall survival (OS) among patients with metastatic renal cell carcinoma (mRCC) treated with targeted monotherapy (VEGF-TKI). Herein, we assess the influence of smoking status on the outcomes of patients with mRCC treated with the current first-line standard of care of immune checkpoint inhibitor (ICI)-based regimens. MATERIALS AND METHODS: Real-world data from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) were collected retrospectively. Patients with mRCC who received either dual ICI therapy or ICI with VEGF-TKI in the first-line setting were included and were categorized as current, former, or nonsmokers. The primary outcomes were OS, time to treatment failure (TTF), and objective response rate (ORR). OS and TTF were compared between groups using the log-rank test and multivariable Cox regression models. ORR was assessed between the 3 groups using a multivariable logistic regression model. RESULTS: A total of 989 eligible patients were included in the analysis, with 438 (44.3%) nonsmokers, 415 (42%) former, and 136 (13.7%) current smokers. Former smokers were older and included more males, while other baseline characteristics were comparable between groups. Median follow-up for OS was 21.2 months. In the univariate analysis, a significant difference between groups was observed for OS (P = .027) but not for TTF (P = .9), with current smokers having the worse 2-year OS rate (62.8% vs 70.8% and 73.1% in never and former smokers, respectively). After adjusting for potential confounders, no significant differences in OS or TTF were observed among the 3 groups. However, former smokers demonstrated a higher ORR compared to never smokers (OR 1.45, P = .02). CONCLUSION: Smoking status does not appear to independently influence the clinical outcomes to first-line ICI-based regimens in patients with mRCC. Nonetheless, patient counseling on tobacco cessation remains a crucial aspect of managing patients with mRCC, as it significantly reduces all-cause mortality.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.047
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.047
GPT teacher head0.337
Teacher spread0.290 · 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 teacher head, 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

Citations5
Published2024
Admission routes1
Has abstractyes

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