The effect of age and gender on bladder cancer: a critical review of the literature
Bibliographic record
Abstract
While patient age and gender are important factors in the clinical decision-making for treating urothelial carcinoma of the bladder (UCB), there are no evidence-based recommendations to guide healthcare professionals. We review previous reports on the influence of age and gender on the incidence, biology, mortality and treatment of UCB. Using MEDLINE, we searched for previous reports published between January 1966 and July 2009. While men are three to four times more likely to develop UCB than women, women present with more advanced disease and have worse survival rates. The disparity among genders is proposed to be the result of a differential exposure to carcinogens (i.e. tobacco and chemicals) as well as reflecting genetic, anatomical, hormonal, societal and environmental factors. Inpatient length of stay, referral patterns for haematuria and surgical outcomes suggest that inferior quality of care for women might be an additional cause of gender inequalities. Age is the greatest single risk factor for developing UCB and dying from it once diagnosed. Elderly patients face both clinical and institutional barriers to appropriate treatment; they receive less aggressive treatment and sub-therapeutic dosing. Much evidence suggests that chronological age alone is an inadequate indicator in determining the clinical and behavioural response of older patients to UCB and its treatment. Epidemiological and mechanistic molecular studies should be encouraged to design, analyse and report gender- and age-specific associations. Improved bladder cancer awareness in the lay and medical communities, careful patient selection, treatment tailored to the needs and the physiological and physical reserve of the individual patient, and proactive postoperative care are particularly important. We must strive to develop transdisciplinary collaborative efforts to provide tailored gender- and age-specific care for patients with UCB.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".