Systemic inflammatory markers as independent prognosticators of head and neck squamous cell carcinoma
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to investigate the prognostic value of the pretreatment inflammatory markers platelet-to-lymphocyte ratio (PLR) and the neutrophil-to-lymphocyte ratio (NLR) in patients with head and neck squamous cell carcinoma (HNSCC). METHODS: We conducted a retrospective analysis of patients diagnosed with HNSCC at McGill University Health Center from 2000 to 2011 (273 patients were retained). Hematologic parameters were recorded within 4 weeks of diagnosis. Mortality and recurrence rates were compared according to various PLR and NLR thresholds. RESULTS: Of the total patients, 20.5% died and 11.0% had disease recurrence. PLR >170 was associated with higher mortality (p = .008). The subgroup with a combination of PLR >170 and NLR ≤3.0 was associated with higher T classification and highest mortality (43%). NLR above 4.2 predicted higher rates of recurrence (p < .0001). The NLR/PLR combination was at least as good as TNM staging in predicting survival. CONCLUSION: PLR is an independent predictor of mortality; NLR is an independent predictor of recurrence in HNSCC. These parameters might be used to identify advanced stages rapidly and economically.
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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 | 0.002 |
| 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".