Systemic inflammatory markers as independent prognosticators of head and neck squamous cell carcinoma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it