The Prognostic Role of Pre-Treatment Neutrophil-to-Lymphocyte Ratio in an Asian Cohort of Patients with Oropharyngeal Squamous Cell Carcinoma
Bibliographic record
Abstract
Purpose: The neutrophil-to-lymphocyte ratio is a simple biomarker that reflects the balance between the systemic inflammatory and immunity status. Here we investigate the prognostic role of pre-treatment neutrophil-to-lymphocyte ratio (NLR) in an Asian cohort of oropharyngeal squamous cell carcinoma (OPSCC) patients. Methods: A retrospective review of OPSCC patients from a tertiary institution was conducted. The NLR was calculated from the haematological specimen taken within a month before treatment. Survival rates were estimated via the Kaplan–Meier method, and Cox proportional hazards regression was performed for univariable and multivariable analyses. The NLR cutpoint was determined using maximally selected log-rank statistics. Results: In a cohort of 148 OPSCC patients, 43% were p16-positive and 44% were p16-negative, with a median follow-up of 24 months. The p16-positive patients were younger (median age 62 vs. 67 years) and exhibited a lower prevalence of heavy smoking (47% vs. 69%). The p16-negative cases frequently presented at an advanced disease stage (74% vs. 41%), with a history of previous radiotherapy (26% vs. 3%). The p16-negative patients displayed a higher median NLR (2.91 vs. 2.49). The 3-year disease-specific survival (DSS) in p16-positive was higher compared to p16-negative patients (89.9% vs. 41.6%). The optimal NLR cutpoint was determined as 3.56 and predicted for decreased DSS (hazard ratio [HR] 2.59, p = 0.004). Multivariable analysis revealed smoking, high NLR ≥ 3.56, and p16-negativity as independent variables associated with poorer DSS and overall survival (OS) across the cohort. Conclusion: A high NLR is independently prognostic of poorer DSS in OPSCC, independent of p16 and smoking status. A NLR of more than 3.56 was highly prognostic for poorer survival and warrants further validation in larger cohorts of OPSCC.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".