Differential prognostic association of systemic inflammatory biomarkers on survival outcomes in head and neck squamous cell carcinoma patients by human papillomavirus status
Bibliographic record
Abstract
Systemic inflammatory response (SIR) markers are prognostic in various cancers. In a prospective cohort study (2006-2019) involving 2044 head and neck squamous cell carcinomas (HNSCC) patients, we assessed the prognostic associations of SIR markers at diagnosis, including NLR (neutrophil-to-lymphocyte ratio), PLR (platelet-to-lymphocyte ratio), LMR (lymphocyte-to-monocyte ratio), NMR (neutrophil-to-monocyte ratio), SII (systemic immune-inflammation index), eosinophil and WBC (white blood cell) levels, with progression-free (PFS) and overall survival (OS). Training (two-thirds randomly selected patients) and withheld test sets were created. Separate multivariable Cox regression models by HPV status were created for each of the seven SIR markers for the training set, and validated in the withheld test set. We found that the majority of SIR markers are strongly and significantly associated with OS and PFS in HPV-positive HNSCC patients, while the results were less significant or of lesser magnitude of association in the HPV-negative HNSCC patients. Despite validating these prognostic associations, the addition of SIR markers to a clinical prognostic model did not significantly improve predictive performance for PFS/OS. Our study demonstrates that SIR markers may have a greater impact on the survival of HPV-positive HNSCC, and less so for HPV-negative HNSCCs. These results suggest differential prognostic impact of inflammation between HPV-driven HNSCCs and non-HPV-driven HNSCCs. Although biologically relevant, these associations do not improve survival prognostication in the clinical setting.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".