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Comparison of outcomes for Hispanic and non-Hispanic patients with advanced renal cell carcinoma in the International Metastatic Renal Cell Carcinoma Database.

2022· article· en· W4286296468 on OpenAlexaff
Kripa Guram, Jiaming Huang, Vishal Navani, Wanling Xie, Talal El Zarif, Elio Adib, Neeraj Agarwal, Haoran Li, Chris Labaki, Muhieddine Labban, José Manuel Ruiz Morales, Toni K. Choueiri, Daniel Yick Chin Heng, Brent S. Rose, Rana R. McKay

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsUniversity of CalgaryOccupational Cancer Research Centre
Fundersnot available
KeywordsMedicineRenal cell carcinomaHazard ratioInternal medicineProportional hazards modelClinical endpointCohortOncologyClear cell renal cell carcinomaCancerConfidence intervalClinical trial

Abstract

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6590 Background: Epidemiologic studies suggest that Hispanic patients with renal cell carcinoma (RCC) have worse outcomes than non-Hispanic White patients (NHW). It is unclear if this disparity is related to inherent biological differences or patients’ social determinants of health (SDOH). Utilizing the International Metastatic Renal Cell Carcinoma Database (IMDC) of patients with RCC primarily receiving care at academic medical centers, we investigated outcomes of Hispanic and NHW patients with advanced RCC. Methods: Eligible patients included patients who self-reported being non-Black Hispanic or NHW with locally advanced or metastatic RCC initiating systemic therapy. The primary endpoint was overall survival (OS) and secondary endpoint was time to treatment failure (TTF) for the first-line therapy. Kaplan Meier curves were constructed for OS and TTF. Cox regression was used to estimate hazard ratios (HR) adjusted for confounding variables. Results: The cohort included 1,563 patients, of which 181 (11.6%) were Hispanic. Most patients were male (74%) with clear cell histology (82%). IMDC risk groups were 18%, 58%, 24% for favorable, intermediate, and poor risk, respectively, and were similar by ethnic groups. Compared to NHW, Hispanic patients were younger at diagnosis (median 57 vs 59 years, p = 0.036), less likely to have > 1 metastatic site (61% vs 77%, p < 0.001) and bone metastases (24% vs 33%, p = 0.009). 1,178 patients (124 Hispanic vs. 1,054 NWH) received treatment before 2018, 385 patients (57 Hispanic vs. 328 NWH) received treatment during or after 2018. With regards to first line therapy, the majority received tyrosine kinase inhibitor (TKI) monotherapy (70%), 10% received immunotherapy (IO) + IO, 9% received TKI + IO, 4% received IO monotherapy, and 8% received other treatments. Median TTF was 7.8 months (95% Confidence Interval (CI): 6.2-9.0) in Hispanic patients and 7.5 months (95% CI: 6.9-8.1) in NHW patients. On multivariable analysis, there was no significant difference in TTF between Hispanic and NHW patients (HR 1.05, 95% CI: 0.89-1.25, p = 0.558). Significant predictors of TTF were presence of unfavorable site of metastases, histology, IMDC risk group, and therapy type. Median OS was 38.0 months (95% CI: 28.1-59.2) in Hispanic patients and 35.7 months (95% CI: 31.9-39.2) in NHW patients. On multivariable analysis, there was no significant difference in OS between Hispanic and NHW patients (HR 1.07, 95% CI: 0.87-1.32, p = 0.544). Significant predictors of OS were number of metastatic sites, presence of unfavorable metastasis, histology, IMDC risk group, and therapy type. Conclusions: In this analysis, we did not detect a difference in OS or TTF for Hispanic patients with RCC. Our data suggest that access to care (as available in a tertiary cancer hospital) can mitigate the historic difference in outcomes in Hispanic versus NWH patients.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.097
GPT teacher head0.374
Teacher spread0.278 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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