Neutrophil‐to‐lymphocyte ratio in head and neck cancer prognosis: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Hematologic markers, such as the neutrophil-to-lymphocyte ratio (NLR), characterize the inflammatory response to cancer and are associated with poorer survival in various malignancies. We evaluate the effect of pretreatment NLR on overall survival (OS) in patients with head and neck squamous cell carcinoma (HNSCC). METHODS: Using multiple databases, a systematic search for articles evaluating the effect of NLR on OS in patients with HNSCC was performed. An inverse variation, random-effects model was used to analyze the data. RESULTS: A total of 24 of 241 articles, including 6479 patients, were analyzed. The combined hazard ratio for OS in patients with an elevated NLR (range 2.04-5) was 1.78 (confidence interval [CI] 1.53-2.07; P < .0001). The hazard ratios for site-specific cancer: oral cavity 1.56 CI 1.23-1.98 (P < .001), nasopharynx 1.66 CI 1.35-2.04 (P < .001), larynx 1.55 CI 1.26-1.92 (P < .001), and hypopharynx 2.36 CI 1.54-3.61 (P < .001). CONCLUSION: An elevated NLR is predictive of poorer OS in patients with HNSCC.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.020 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".