Pre-treatment neutrophil-to-lymphocyte ratio as predictor of adverse outcomes in patients undergoing radical cystectomy for urothelial carcinoma of the bladder
Bibliographic record
Abstract
BACKGROUND: An elevated neutrophil-to-lymphocyte ratio (NLR) is associated with poor outcome in various tumours. Its prognostic utility in patients with urothelial carcinoma of the bladder (UCB) undergoing radical cystectomy (RC) is yet to be fully elucidated. METHODS: A cohort of patients undergoing RC for UCB in a tertiary referral centre between 1992 and 2012 was analysed. Neutrophil-to-lymphocyte ratio was computed using complete blood counts performed pre-RC, or before neo-adjuvant chemotherapy where applicable. Time-dependent receiver operating characteristic curves were used to determine the optimal cutoff point for predicting recurrence-free survival (RFS), cancer-specific survival (CSS) and overall survival (OS). The predictive ability of NLR was assessed using Kaplan-Meier analyses and multivariable Cox proportional hazards models. The likelihood-ratio test was used to determine whether multivariable models were improved by including NLR. RESULTS: The cohort included 424 patients followed for a median of 58.4 months. An NLR of 3 was determined as the optimal cutoff value. Patients with an NLR⩾3.0 had significantly worse survival outcomes (5y-RFS: 53% vs 64%, log-rank P=0.013; 5y-CSS: 57% vs 75%, log-rank P<0.001; 5y-OS: 43% vs 64%, log-rank P<0.001). After adjusting for disease-specific predictors, an NLR ⩾3.0 was significantly associated with worse RFS (HR=1.49; 95% CI=1.12-2.0, P=0.007), CSS (HR=1.88; 95% CI=1.39-2.54, P<0.001) and OS (average HR=1.67; 95% CI=1.17-2.39, P=0.005). The likelihood-ratio test confirmed that prognostic models were improved by including NLR. CONCLUSIONS: Neutrophil-to-lymphocyte ratio is an inexpensive prognostic biomarker for patients undergoing RC for UCB. It offers pre-treatment prognostic value in addition to established prognosticators and may be helpful in guiding treatment decisions.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".