Delays to Diagnosis and Management of Upper Tract Urothelial Carcinoma
Bibliographic record
Abstract
Background and Objective Upper tract urothelial carcinoma (UTUC) is rare in comparison to urothelial carcinoma of the bladder or renal cell carcinoma. UTUC may present with loin pain, haematuria or alternatively can be identified as an incidental finding on imaging. There are often delays to diagnosis as haematuria clinics are efficient for bladder and renal cancer but less effective for UTUC. The diagnosis and treatment of UTUC is more challenging, as it often requires two operations and multiple MDT discussions. Diagnosis must be certain to avoid unnecessary radical surgery. We found that our patients were experiencing significant delays to definitive surgery. Our patients currently follow the pathway for bladder and renal cancer, as there is no UTUC pathway at or trust or published in the literature. We audited our diagnostic pathway to see how we could tailor the pathway to be more effective for patients with UTUC. This will ensure that more patients will meet the NHS 62-day targets. Materials and Methods A retrospective review of patients management pathway from December 2008 to December 2018. Patients were identified by the pathological code for UTUC. Results A total of 62 patients underwent nephroureterectomy during a 10-year period. 48 patients were analysed. The median waiting time for haematuria clinic from referral was 21days, a further 73 days to ureterorenoscopy and biopsy, and then 14 days to definitive nephroureterectomy. Only one patient met the NHS 62-day treatment target. Our waiting times are comparable with other published international series. We have implemented a new UTUC pathway to streamline the diagnosis and management of UTUC. Some patients with UTUC will still have inevitable delays as diagnosis can be very challenging but this new pathway should improve the patient journey and reduce the waiting times significantly.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".