Pretreatment Neutrophil‐to‐Lymphocyte Ratio ( <scp>NLR</scp> ) Predicts Treatment Toxicity and Intolerance in Operable Head and Neck Cancer: An Ambispective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Pretreatment neutrophil to lymphocyte ratio (NLR) is a negative prognostic marker for survival in head and neck cancer (HNC). Its association with treatment toxicity and intolerance in patients undergoing curative-intent surgery is unknown. METHODS: Ambispective study of patients with operable stage II-IV, HPV-negative HNC treated at two academic hospitals. Multiple logistic regression was performed to evaluate the association between pretreatment NLR and short-term treatment outcomes including treatment toxicity and intolerance. RESULTS: Among 456 patients, 194 experienced treatment-related toxicity, defined as grade ≥ 3 adverse events, and 200 exhibited treatment intolerance, defined as treatment discontinuation or delay due to poor tolerance. High pretreatment NLR was significantly associated with an increased risk of toxicity (OR 1.04; 95% CI: 1.00-1.09) and intolerance (OR 1.06; 95% CI: 1.01-1.12). Patients with an NLR < 2.5 had significantly lower odds of experiencing toxicity (OR 0.50; 95% CI: 0.26-0.96) and intolerance (OR 0.57; 95% CI: 0.33-0.99) after adjusting for multiple confounders. CONCLUSIONS: Low pretreatment NLR was associated with reduced treatment toxicity and lower rates of intolerance in patients undergoing curative-intent surgery for HNC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".