Replacing surveillance cystoscopy with urinary biomarkers in followup of patients with non-muscle-invasive bladder cancer: Patients’ and urologic oncologists’ perspectives
Bibliographic record
Abstract
INTRODUCTION: Urinary biomarkers are being developed to detect bladder cancer recurrence/progression in patients with non-muscle-invasive bladder cancer (NMIBC). We conducted a questionnaire-based study to determine what diagnostic accuracy and cost would such test(s) need for both patients and urologic oncologists to comfortably forgo surveillance cystoscopy in favour of these tests. METHODS: Surveys were administered to NMIBC patients at followup cystoscopy visit and to physician members of the Society of Urologic Oncology. Participants were questioned about acceptable false-negative (FN) rates and costs for such alternatives, in addition to demographics that could influence chosen error rates and costs. RESULTS: A total of 137 patient and 51 urologic oncologist responses were obtained. Seventy-seven percent of patients were not comfortable with urinary biomarker(s) alternatives to repeat cystoscopy, with a further 14% willing to accept such alternatives only if the FN rate were 0.5% or lower. Seventy-five percent of urologic oncologists were comfortable with an alternative urinary biomarker test(s), with 37% and 33% willing to accept FN rates of 5% and 1%, respectively. Forty-seven percent of patients were not willing to pay out-of-pocket for such tests, while 61% of urologic oncologists felt that a price range of $100-500 would be reasonable. CONCLUSIONS: This is the first survey evaluating patient and urologic oncologist perspectives on acceptable error rates and costs for urinary biomarker alternatives to surveillance cystoscopy for patients with NMIBC. Despite potential responder bias, this study suggests that urinary biomarker(s) will require sensitivity equivalent to that of cystoscopy in order to completely replace it in surveillance of patients with NMIBC.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".