The Implementation of a Risk Stratification Tool for the Haematuria Clinic to Optimise the Management of Patients with High-Risk Bladder Cancer in the COVID-19 Era
Bibliographic record
Abstract
Introduction: Elective waiting lists have become more stretched because of the COVID-19 pandemic and patients have evidently been waiting longer for treatment. Patients with high-risk bladder cancer require timely treatment and there is strong evidence to suggest that delay in treatment contributes to a risk of disease progression, metastases and death. Studies have shown that bladder tumour appearances at flexible cystoscopy (FC) can accurately predict high-risk disease on histopathology following transurethral resection. An opportunity for service improvement resulted in a review of the practice followed by the authors and the development of a risk stratification tool for the haematuria clinic which aimed to prioritise the pathway of those with high-risk disease. Materials and methods: A risk stratification tool was developed for patients with newly diagnosed bladder tumours at the haematuria clinic. A tumour assessment carried out at FC is used to predict patients with high-risk disease, thus allowing those patients to be prioritised over those with low-risk disease on the waiting list. It also includes a reminder to request staging investigations for those with suspected high-risk disease. A closed loop audit was carried to review the following: the quality of tumour risk assessment at the haematuria clinic; time from FC to transurethral resection of bladder tumour (TURBT); concordance between tumour assessment at FC and histopathology after TURBT; efficiency of arranging early staging investigations for those with suspected high-risk bladder cancer; time from FC to staging CT scan. Results: A risk assessment was carried out for 93% of patients in the second cycle compared with 40% in the first cycle. Concordance was noted in 83% of those with confirmed high-risk non-muscle invasive bladder cancer (NMIBC) and 83% of muscle invasive bladder cancer (MIBC) in the first cycle, and in 100% of patients with high-risk NMIBC and MIBC in the second cycle. The interval from FC to TURBT decreased from 27 days in the first cycle to 21 days in the second cycle in those with high-risk NMIBC, and from 27 to 13 days in those with MIBC. Time from FC to staging CT for patients with high-risk bladder cancer was 6 days in the first cycle and 3 days in the second cycle if the request was made from the haematuria clinic. If the CT scan was requested later, the interval increased to 39 days in the first cycle and 22 days in the second cycle. Conclusion: There is a high degree of concordance between tumour risk assessment at FC and final pathology following TURBT which is supported by several series. Performing risk assessment and requesting staging investigations at the haematuria clinic for patients with newly diagnosed high-risk bladder cancer can minimise delays in their treatment pathway and improve patient outcomes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".