Bibliographic record
Abstract
Introduction and Objectives: Upper tract urothelial carcinoma (UTUC) is a rare but increasingly prevalent cancer in Australia.Australian data on presentation, diagnosis, treatment and outcomes are limited.Methods: This study retrospectively assessed patients diagnosed with UTUC at a single Victorian health service between 2015 and 2020.Results: 75 patients were diagnosed with UTUC in this period.62.7% of patients were male.Average age was 77.7 years at diagnosis.Median Charlson Comorbidity Index was 4 and ECOG 1.Previous bladder cancer was present in 27.4% and UTUC in 4.1%.Symptoms prompted presentation in 61.8% of patients.Diagnostic evaluation included CT-IVP (73.6%), urine cytology (51.4%) and endoscopic assessment (72.6%).MDM discussion occurred in 94.7% of patients.Radical nephroureterectomy (RNU) was performed in 54.6% of patients of which 2.4% underwent neoadjuvant chemotherapy.Pathological staging at RNU was T0 in 4.7% (n=2), Ta in 34.9% (n=15), Tis in 7.0% (n=3), T1 in 9.3% (n=4), T2 in 11.6% (n=5), T3 in 25.6% (n=11) and T4 in 7.0% (n=3).Adjuvant chemotherapy was utilised in 2.4% of those who underwent RNU.Kidney sparing surgery occurred in 17.3% of patients.Recurrence occurred in 41.9% of patients who underwent radical treatment.Distant metastasis (50%), bladder (28%), local lymph node (22%) and contralateral upper tract (22%) were common sites of recurrence.Palliative systemic therapy was utilised in 17.3% of patients.Other palliative interventions included longterm ureteric stenting (20%), permanent nephrostomy (6.8%) and radiotherapy (13.3%).45.8% of patients are deceased at varying degrees of follow-up from 1-5 years.Of these 75.8% died from progression of their UTUC.Active disease was present in 25.6% of those who remained alive.Conclusions: Outcomes of patients with UTUC are often poor.An Australian multicentre UTUC registry has the potential to identify patterns of care, compare outcomes of individual treatment strategies and provide clinician feedback.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.429 | 0.115 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".