Understanding patient perspectives in the management of their muscle‐invasive bladder cancer
Bibliographic record
Abstract
OBJECTIVES: To evaluate the impact of advocacy efforts by organisations such as the Bladder Cancer Advocacy Network (BCAN) to increase awareness about the signs, symptoms, diagnosis, treatment and need for a multidisciplinary approach to the care of patients with muscle-invasive bladder cancer (MIBC). MATERIALS AND METHODS: We developed a 32-question survey with input from physicians, nurses, patients and caregivers. The survey was posted on the BCAN website between August 2013 and September 2014 (Cohort A) and again between June 2023 and April 2024 (Cohort B). Questions focused on time from initial symptoms to diagnosis and treatment, proportion seeing a medical oncologist or radiation oncologist, treatments offered/received, and patient satisfaction with their treatment choices. RESULTS: Overall, 337 self-selected patients mostly white males from the US or Canada, with at least an undergraduate education completed the survey. There were 243 patients in Cohort A and 94 patients in Cohort B. The median age (range) at diagnosis was 61 (31-93) in Cohort A and 63 (38-87) in Cohort B. The most common presenting symptom was hematuria. In Cohorts A and B, 35% vs 30% (P = 0.56) waited >3 months to seek medical attention, and in 38% vs 43% (P = 0.76) it took >3 months to obtain a diagnosis. Men were more likely than women to be diagnosed within 1-2 months in both cohorts (Cohort A: 68% vs 47%; P = 0.03, Cohort B: 70% vs 37%; P = 0.03). Preoperative consultation with a radiation oncologist and bladder-sparing use were infrequent. More patients in Cohort A (77%) felt they had enough time to make decisions compared to Cohort B (67%) (P = 0.004). Most patients were satisfied with their treatment choices. CONCLUSIONS: There are ongoing areas of unmet need in MIBC, including reducing time to definitive diagnosis and treatment, especially in women, and increasing multidisciplinary assessments prior to definitive surgery. Respondents were self-selected, had access to the BCAN website, and were highly educated, potentially limiting the generalizability of these results.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".