The preoperative neutrophil-to-lymphocyte ratio predictive value for survival in patients with brain metastasis
Bibliographic record
Abstract
Abstract Background The neutrophil-to-lymphocyte (NLR), monocyte-to-lymphocyte (MLR), platelet-to-lymphocyte ratio (PLR), and red blood cell distribution width (RDW) have been previously studied as predictors of survival in different malignancies. Objective The aim of this study was to evaluate the predictive value of these hematologic inflammatory biomarkers for patients with brain metastases (BM). Methods We reviewed a consecutive cohort of patients at Instituto do Cancer do Estado de São Paulo (ICESP-FMUSP) from 2011 to 2016 with ≥ 1 BM treated primarily by surgical resection. The primary outcome was 1-year survival. We optimized the NLR, MLR, PLR, and RDW cutoff values, preserving robustness and avoiding overestimation of effect size. Results A total of 200 patients (mean age 56.1 years; 55.0% female) met inclusion criteria. Gross-total resection was achieved in 89.0%. The median (quartiles) preoperative and postoperative KPS scores were 60 (50–80) and 80 (60–90), respectively. Preoperative NLR was significantly associated with survival (HR 2.66, 95% CI: 1.17–6.01, p = 0.019). A NLR cutoff value of 3.83 displayed the most significant survival curve split. Conclusions Preoperative NLR is an independent predictor of survival in newly diagnosed BM. We propose a cutoff value of 3.83 for preoperative NLR testing may be clinically useful as predictor of poor survival in this population. The wide accessibility of the NLR favors its inclusion in clinical decision-making processes for BM management.
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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 | 0.001 | 0.001 |
| 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".