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 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".