Patient perceptions about their treatment for muscle-invasive bladder cancer (MIBC) over the last decade.
Bibliographic record
Abstract
765 Background: The Bladder Cancer Advocacy Network (BCAN) is a national patient advocacy organization that has promoted awareness about the signs, symptoms, diagnosis, treatment and need for a multidisciplinary approach to the care of patients with MIBC. To better understand the impact of these efforts, and patients’ perceptions of their care, we surveyed bladder cancer patients initially in 2013 and again in 2023 through the BCAN website. Methods: We developed a 32-question survey with input from physicians, nurses, patients and caregivers. The survey was posted on the BCAN website 06/13-10/14 (Cohort A) and again from 06/23-04/24 (Cohort B). Questions focused on time from initial symptoms to diagnosis and treatment; proportion seeing a medical oncologist (MO) or radiation oncologist (RO); and treatments offered/received. Results: Overall, 337 (243 and 94) patients completed the survey. Respondents were self-selected and most were from the US/Canada, male, white, and had at least an undergraduate education. Median age was 61 (cohort A; range 31-93) and 63 (cohort B; range 38-87), respectively, at the time of diagnosis. The most common presenting symptom was hematuria (88% vs 78%, p=0.05). Overall, 35% vs 30% (p=0.56) waited > 3 months to seek medical attention; in 38% and 43% it took > 3 months to obtain a pathologic diagnosis (p=0.76). Men were more likely than women to be diagnosed within 1-2 months of seeking medical attention: Cohort A (68% vs 47% p=0.03) and Cohort B (70% vs 37%, p=0.03). In both cohorts, prior to surgery, approximately 50% saw a medical oncologist and were offered neoadjuvant chemotherapy; however, less than 10% saw a radiation oncologist and were offered bladder sparing approaches. Most patients reported having all the necessary information, knowing the right questions to ask, and were satisfied with their choices. A higher proportion in Cohort A vs Cohort B felt they had enough time to make their decisions (77% vs 67%, p=0.004). Conclusions: Despite nearly two decades of advocacy, there are ongoing areas of unmet need in MIBC. These include 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".