The significance of isolated de novo red patches in the bladder in patients referred with suspected urinary tract cancer: Results from the IDENTIFY study
Bibliographic record
Abstract
Objectives: To assess the contemporary malignancy rate in isolated de novo red patches in the bladder and associated risk factors for better selection of red patch biopsy. Patients: Patients from the IDENTIFY dataset; Patients referred to secondary care with suspected urinary tract cancer and found to have isolated de novo red patches on cystoscopy. Methods: We reported the unadjusted cancer prevalence in isolated de novo red patches that were biopsied; multivariable logistic regression was used to explore cancer-associated risk factors including age, sex, smoking, type of haematuria, LUTS, UTIs and a suspicious-looking red patch (as reported by the cystoscopist). Sub-analysis of these by clinical role and experience was performed. Results: A total of 1110 patients with isolated de novo red patches were included. 41.5% (n = 461) were biopsied, with a malignancy rate of 12.8% (59/461), which was significantly higher in suspicious versus non-suspicious red patches (19.1% vs. 2.81%, p < 0.01). There was a significant association between bladder cancer and age (OR 1.04, 95% CI 1.01-1.07, p = 0.01), smoking history (OR 2.62, 95% CI 1.09-6.27, p = 0.03) and suspicious-looking patch (OR 6.50, 95% CI 2.47-17.1, p < 0.01). The majority of malignancies were in over 60-year-olds. Malignancy rates in suspicious versus non-suspicious red patches did not differ significantly between clinical roles or experiences.Limitations included subjectivity in classifying a suspicious patch and selection bias as not all patches were biopsied. Conclusions: Many patients still undergo unnecessary biopsies under general anaesthetic for isolated de novo red patches. Clinicians should consider the patient's age, smoking status and how suspicious-looking the patch is, before deciding on surveillance versus biopsy to improve cancer diagnostic yield.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".