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Sex differences in bladder cancer outcomes among smokers with advanced bladder cancer

2011· article· en· W1543120166 on OpenAlexaffabout
Peter J. Boström, Sultan Alkhateeb, Greg Trottier, Paul Athanasopoulos, Tuomas Mirtti, Hannes Kortekangas, Matti Laato, Bas van Rhijn, Theodorus van der Kwast, Neil Fleshner, Michael A.S. Jewett, Antonio Finelli, Alexandre R. Zlotta

Bibliographic record

VenueBritish Journal of Urology · 2011
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsUniversity of TorontoMount Sinai HospitalPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineBladder cancerCystectomySmoking cessationCancerInternal medicineCohortOncologyPathology

Abstract

fetched live from OpenAlex

Study Type – Aetiology (individual cohort) Level of Evidence 2b What's known on the subject? and What does the study add? Smoking is well described among the most important risk factors for bladder cancer. It is also known that higher quantity of tobacco exposure is associated with higher bladder cancer risk and that smoking cessation is known to be associated with lower risk of bladder cancer. Furthermore, among patients with non‐muscle invasive bladder cancer, smoking cessation decreases the risk of tumour recurrence. On the other hand, the effect of smoking on tumour stages at presentation and especially on prognosis is not well studied. The current study describes the presentation and outcome of 564 patients (64% smokers, 36% non‐smokers) treated with radical cystectomy. Patients with smoking history have more advanced outcome at the time of radical surgery and significantly worse outcome after surgery when compared to non‐smokers, although the effect of smoking was not significant when survival was studied in multivariable analysis including classic prognostic parameters such as tumour grade, stage and adjuvant chemotherapy. Finally, there was a surprising finding that history of smoking affected outcome among male patients but such effect was not noted among female patients. OBJECTIVE • To study the effect of smoking on bladder cancer presentation and outcome in a large cystectomy population. PATIENTS AND METHODS • A database including 546 patients from the University Health Network (Toronto, Canada) and Turku University Hospital (Turku, Finland) was studied. • In addition to the association of smoking with clinicopathological parameters, the effect of smoking on survival was analyzed. • Categorical data were analyzed by the chi‐squared test and numerical data were analyzed by Student's t ‐test. • The Kaplan–Meier method, log‐rank test and a proportional hazards model were used to estimate the effect of smoking on survival. RESULTS • In total, 352 patients (64%) were smokers and 194 (36%) were non‐smokers. • Smokers had more frequently advanced tumours and nodal metastasis. • The 10‐year disease‐specific survival (DSS) was 52% vs 66% for smokers and non‐smokers, respectively ( P = 0.039). • Smokers also had significantly worse overall survival (10‐year overall survival 37% vs 62%; P = 0.015). • Smoking affected significant DSS among men ( P = 0.012), although no effect was observed among women. • In a univariate model smoking was associated with a hazard ratio (HR) of 1.4 (95% confidence interval, CI, 1.0–1.9) for bladder cancer specific mortality and 1.4 (95% CI, 1.1–1.8) for overall mortality. • In a multivariate model, smoking did not impact on DSS (HR, 1.1; 95% CI, 0.8–1.6; P = 0.41). • In addition to advanced stage and nodal metastasis, female sex was an independent risk factor for DSS (HR, 1.6; 95% CI, 1.1–2.3; P = 0.007). CONCLUSIONS • Smokers appear to have worse outcomes after radical cystectomy for bladder cancer; however, it does not appear to be an independent prognostic factor for survival. • Smoking affected survival only among men. • Women had poorer survival but smoking was not a contributing factor to this.

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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.276
Teacher spread0.250 · 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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Citations31
Published2011
Admission routes2
Has abstractyes

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