Are there differences between de novo and secondary upper tract urothelial carcinoma tumours?
Bibliographic record
Abstract
INTRODUCTION: Upper tract urothelial carcinoma (UTUC) accounts for <5% of all urothelial cancers. We aimed to ascertain the clinical differences between UTUC tumours presenting de novo (DnUTUC) and those presenting secondary (SUTUC) following a bladder cancer diagnosis. METHODS: Our institutional database was queried for all UTUC patients who were surgically treated with radical nephroureterectomy or ureterectomy between 2003 and 2017. Bladder recurrence and cancer-specific mortality were compared. To reduce the possible bias due to confounding variables obtained from a simple comparison of outcomes, DnUTUC patients were matched (for age, gender, tumour location, type of surgery, grade, TNM staging, presence of carcinoma in situ, and lymphovascular invasion) with propensity score to SUTUC patients. Bladder recurrence and cancer-specific mortality were assessed with Cox proportional hazards model. RESULTS: A total of 117 UTUC patients were identified: 80 with DnUTUC (68.4%) and 37 with SUTUC (31.6%). A greater proportion of males with SUTUC was demonstrated (89.2% vs. 68.8; p=0.02). In both groups, 67.5% of patients had high-grade disease, but SUTUC demonstrated a higher carcinoma in situ rate (43.2% vs. 25%; p=0.047). Univariate analysis demonstrated that the five-year bladder recurrence rate was trending to be higher in SUTUC (65.3% vs. 20.5%; p=0.099). In the Cox model, however, it was associated with increased bladder recurrence (hazard ratio [HR] 3.69; 95% confidence interval [CI] 1.68-8.09; p=0.001). Although univariate analysis demonstrated that SUTUC patients were more likely to die of their disease (30.6% vs. 9%; p=0.009), the multivariable Cox model did not demonstrate this association. The limitations of this study include its retrospective, single-centre design and relatively small cohort of patients. CONCLUSIONS: In this hypothesis-generating study, some evidence suggests that further research is needed to delineate differences between SUTUC and DnUTUC.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".