Outcomes and prognostic factors in patients with a single lymph node metastasis at time of radical cystectomy
Bibliographic record
Abstract
OBJECTIVES: To identify clinicopathological factors that predict outcomes in patients with a single lymph node (LN) metastasis (pN1) treated with radical cystectomy (RC) for urothelial carcinoma of the bladder (UCB). LN metastasis is an established predictor of clinical outcomes in patients. While most patients with large LN burden experience disease recurrence, lymphadenectomy can be curative in patients with pN1 disease. PATIENTS AND METHODS: We analysed 381 patients with pN1 UCB from a multi-institutional cohort of 4335 patients with UCB treated with RC and lymphadenectomy without preoperative chemo- or radiotherapy. Subgroup analyses were performed for patients with ≥9 LNs removed and according to adjuvant chemotherapy administration (n = 215). RESULTS: The median (interquartile range, IQR) LN number was 15 (19) and the median (IQR) LN density was 6.7 (7.5)%. Within a median follow-up of 41 months, the mean (+/- SD) 2- and 5-year cancer-specific survival (CSS) rates were 55 (3)% and 46 (3)%, respectively. On multivariable analysis that adjusted for the effects of standard clinicopathological features, female gender (hazard ratio [HR] 1.48, P = 0.023), higher tumour stage (HR 1.68, P = 0.007), positive soft tissue surgical margin (STSM; HR 2.06, P = 0.004), higher LN density (HR 2.99, P = 0.025) and absence of adjuvant chemotherapy (HR 0.70, P = 0.026) were independently associated with CSS. In subgroup analyses of patients with ≥9 LNs removed, tumour stage and STSM status remained independent predictors for CSS (P = 0.009 and P < 0.001, respectively). CONCLUSIONS: About half of the patients with pN1 UCB died from UCB within 5 years of RC. Pathological stage and STSM status are strong predictors for outcomes. Accurate prediction of the individual risk of CSS may help risk stratifying pN1 UCB in order to help improve clinical-decision making. Patients with pN1 UCB presenting with additional unfavourable risk factors need a closer follow-up scheduling and might receive adjuvant therapy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".