Lymphocyte‐to‐monocyte ratio and neutrophil‐to‐lymphocyte ratio as biomarkers for predicting lymph node metastasis and survival in patients treated with radical cystectomy
Bibliographic record
Abstract
PURPOSE: To evaluate the role of lymphocyte-to-monocyte ratio (LMR) and neutrophil-to-lymphocyte ratio (NLR) as pre-operative markers for predicting extravesical disease and survival outcomes in patients undergoing radical cystectomy (RC) for urothelial carcinoma of the bladder (UCB). MATERIALS AND METHODS: Data from 4335 patients undergoing RC for clinically non-metastatic UCB were analyzed. Multivariable logistic regression models were used to predict lymph node involvement and extravesical disease (defined as ≥pT3 and N0). Recurrence-free (RFS), cancer-specific (CSS), and overall survival (OS) were evaluated using multivariable Cox models. The accuracy of the models was assessed with receiver operating characteristics (ROC) curves and concordance-index. RESULTS: Median LMR was 3.5 and median NLR was 2.7. Addition of LMR and NLR to a standard preoperative model improved its discrimination for prediction of lymph node metastasis by 4.5%. On multivariable analysis LMR and NLR independently predicted RFS, CSS, and OS. The discrimination of this model increased by adding LMR and NLR but was not significant. CONCLUSIONS: LMR and NLR independently improved the preoperative prediction of lymph node metastasis and survival outcomes. As they are readily available, they could be integrated in a panel of preoperative markers helping selecting patients who have extravesical lymph node involvement and more aggressive disease.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".