Lymphocyte‐to‐monocyte ratio and neutrophil‐to‐lymphocyte ratio as biomarkers for predicting lymph node metastasis and survival in patients treated with radical cystectomy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 it