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MP22-05 SEX DIFFERENCES IN MUSCLE-INVASIVE BLADDER CANCER WITH RADICAL CYSTECTOMY

2024· article· en· W4394809050 on OpenAlexaboutno aff
Kiera Liblik, Marlo Whitehead, Robert Siemens

Bibliographic record

VenueThe Journal of Urology · 2024
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsCystectomyBladder cancerMedicineProportional hazards modelCancerRetrospective cohort studyCancer registryCohortGynecologyInternal medicineOncology

Abstract

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You have accessJournal of UrologyBladder Cancer: Invasive II (MP22)1 May 2024MP22-05 SEX DIFFERENCES IN MUSCLE-INVASIVE BLADDER CANCER WITH RADICAL CYSTECTOMY Kiera Liblik, Marlo Whitehead, and Robert Siemens Kiera LiblikKiera Liblik , Marlo WhiteheadMarlo Whitehead , and Robert SiemensRobert Siemens View All Author Informationhttps://doi.org/10.1097/01.JU.0001008608.50694.4b.05AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Emerging research in muscle-invasive bladder cancer (MIBC) suggests that female patients have delayed presentation, differential treatment response, less guideline-concordant care, and worse survival outcomes as compared to male patients. The actual degree of these differences and contributing factors are poorly understood due to a historical paucity of sex-based analysis. The present study analyzes sex differences in a large cohort of patients who underwent radical cystectomy (RC). METHODS: This is a retrospective, population-based study of all MIBC patients that underwent RC in Ontario, Canada between 2009-2013 utilizing records linked to the Ontario Cancer Registry. The primary objective was to assess sex differences in treatment variables and outcomes including downstaging, cancer-specific survival (CSS), and overall survival (OS). Cox proportional-hazards regression models were used to adjust for known confounders. RESULTS: In total, 1,573 patients were included (32.9% female) with no sex differences in baseline characteristics. The final pathological stage was higher in female than male patients (82% versus 77% ≥pT2). Perioperative management was similar between sexes, including wait times, chemotherapy use, and multi-disciplinary consultations. Although, female patients were less likely to undergo a pelvic lymph node dissection (PLND) than male patients (91% vs. 95%; p=0.007). The downstaging rate was also higher in male patients (10.8%) than in female patients (8.1%). Univariate analysis demonstrated a statistically non-significant female versus male differential in CSS (Hazard Ratio 1.17 (95% Confident Interval [CI] 0.99-1.37); p=0.06) and OS (HR 1.16 (95%CI 1.00-1.34); p=0.05). After adjusting for confounders, there was no difference in OS between female versus male patients (HR 1.07; p=0.33), including when only looking at T2+ patients (HR 1.16; p=0.09). CONCLUSIONS: This study represents an important addition to the literature on sex differences in MIBC patients undergoing RC in real-life practice. Although there were no sex differences in perioperative care, there were lower rates of PLND and pathological downstaging in female patients. Absolute survival differences were not appreciated; however, this did not adjust for a higher average life expectancy in female as compared to male Canadians. The results in this modest cohort suggest that the relative survival of female patients is inferior, warranting further investigation. These observations underscore the need to report bladder cancer outcomes by sex as opposed to only using sex as a model variable. Source of Funding: None © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e342 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Kiera Liblik More articles by this author Marlo Whitehead More articles by this author Robert Siemens More articles by this author Expand All Advertisement PDF downloadLoading ...

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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)0.000
metaresearch head score (Gemma)0.002
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 score0.267
Threshold uncertainty score0.530

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0200.001

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 head0.020
GPT teacher head0.286
Teacher spread0.266 · 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
Published2024
Admission routes1
Has abstractyes

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