Optimal Cutoff for the Neutrophil-to-Lymphocyte Ratio as a Tool for Pre-chemotherapy Prognosis Stratification of Breast Cancer Patients
Bibliographic record
Abstract
Introduction The neutrophil-to-lymphocyte ratio (NLR) is an established inflammatory marker in cancer patients. The optimal cut-off as an independent prognostic factor for breast cancer (BC) progression in patients undergoing chemotherapy remains debatable, hindering effective stratification. This study explored the optimal NLR cut-off by comparing various thresholds and assessing their effectiveness in stratifying BC patients according to prognosis. Methods This was a longitudinal quantitative study conducted at Queen's University in Kingston, Ontario, Canada, and the associated hospital is Kingston General Hospital. Demographic, clinical, and cancer-specific data on 42 BC patients were recorded, including complete blood counts before and after two cycles of chemotherapy. The receiver operating characteristic curve assessed discriminatory performance. Diagnostic metrics and Youden's J index were calculated, and McNemar's test was used to compare baseline NLR cutoffs of 2.5, 3.0, and 3.5. Kaplan-Meier curves assessed the relationship between various NLR cut-offs and other cancer prognostic markers. Results The three NLR cutoffs demonstrated distinct diagnostic metrics and Youden's J index values (p < 0.001), with the 3.0 cutoff providing the most balanced performance. Patients with pre-chemotherapy NLR > 3.0 were predicted to develop advanced stage BC more rapidly compared to those with pre-chemotherapy NLR < 3.0. Conclusion We believe that a more stringent NLR cutoff of 3.0 may be a suitable predictor of prognosis in BC patients based on the ranges evaluated in the literature. Findings of this paper could help clinicians in stratifying BC patients by risk, improving personalized treatment intensity while monitoring strategies accordingly.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".