Marital status: a gender‐independent risk factor for poorer survival after radical cystectomy
Bibliographic record
Abstract
UNLABELLED: Study Type - Prognosis (cohort) Level of Evidence 2a. What's known on the subject? and What does the study add? Married individuals have lower morbidity and mortality rates for all major causes of death. Cancer-specific survival is better in married patients with testis cancer, prostate cancer, breast cancer, cervical cancer, as well as head and neck cancers. We have found the effect of marital status on outcomes after radical cystectomy to be variable, depending on gender and the outcome addressed. Being married is predictive of lower all-cause mortality for both men and women relative to their separated, divorced or widowed (SDW) or never-married counterparts. It is also predictive of lower bladder-cancer-specific mortality relative to SDW individuals. Marriage also exerts a protective effect on men regarding non-organ-confined disease, with those never having married having significantly higher rates. OBJECTIVES: • To examine the effect of marital status (MS) on the rate of non-organ-confined disease (NOCD) at radical cystectomy (RC) • To assess the effect of MS on the rate of bladder-cancer-specific mortality (BCSM) and all-cause mortality (ACM) after RC for urothelial carcinoma of the urinary bladder (UCUB). MATERIALS AND METHODS: • A total of 14 859 patients, who underwent RC for UCUB, were captured within the Surveillance, Epidemiology, and End Results database, between 1988 and 2006. • Logistic regression analysis was used to assess the rate of NOCD (T(3-4) /N(I-3) /M(0) ) at RC and Cox regression analyses were used to assess BCSM and ACM. • Analyses were stratified according to gender; covariates included socio-economic status, tumour stage, age, race, tumour grade and year of surgery. RESULTS: • Never-married males had a higher rate of NOCD at RC (odds ratio = 1.22, P= 0.004), an effect not found in never-married females. • Separated, divorced or widowed (SDW) males (hazard ratio [HR]= 1.18, P= 0.005) and females (HR = 1.16, P= 0.002) had higher rates of BCSM than their married counterparts. • SDW and never-married males had higher rates of ACM than their married counterparts (HR = 1.22, P < 0.001 and HR = 1.26, P < 0.001, respectively). • SDW and never-married females also had higher rates of ACM than married females (HR = 1.24, P < 0.001 and HR = 1.22, P= 0.01, respectively). CONCLUSIONS: • For both men and women, being SDW conveyed an increased risk of BCSM after RC. • SDW and never marrying had a deleterious effect on ACM. • Unfavourable stage at RC was also seen more commonly in never-married males.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".