Sexual function of patients under surveillance for bladder cancer
Bibliographic record
Abstract
OBJECTIVE To describe the prevalence of sexual dysfunction and evaluate risk factors in patients just diagnosed with non-muscle-invasive bladder cancer (NMI UC), who have the prospect of an intensive surveillance scheme by cysto-urethroscopy to detect tumour recurrences. PATIENTS AND METHODS We conducted a cross-sectional survey on 150 patients just diagnosed with primary or recurrent NMI UC. Patients were participating in a randomized clinical multicentre trial (CEFuB), comparing two surveillance schemes. Patients were asked to complete questionnaires at study entry 3 months before the start of the study-surveillance scheme (demographic characteristics, a validated visual analogue scale, and validated subset of questions on sexual function and performance derived from QLQ-BLS-24). The results were compared with those from an age-and gender-matched healthy population. RESULTS The response rate was 95% (142/150); 61% (87/142) of the respondents were sexually active in the previous 4 weeks after diagnosis, 66% (70/105) of men and 46% (17/37) of women. Although libido was not negatively affected, 54% (47/87) of the patients had a sexual dysfunction, and 23% (17/73) were afraid to inflict harm on their partner by sexual contact. Sexually active patients perceived a higher state of general health (P = 0.03). CONCLUSIONS The prevalence of sexual dysfunction in patients with NMI UC is very high (54%) compared with an age- and gender-matched healthy population (20-45%). No predictors for sexual dysfunction were found. These patients and partners would benefit from proper sexual information in the outpatient clinic.
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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 | 0.000 | 0.003 |
| 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 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".