Prognostic value of changes in pre- and postoperative inflammatory blood markers in HPV-negative head and neck squamous cell carcinomas
Bibliographic record
Abstract
Objective: Neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR) are inflammatory markers easily obtained from a routine complete blood count, and their preoperative values have recently been correlated with oncological outcomes in patients with HPV-negative head and neck squamous cell carcinoma (HNSCC). The aim of this study is to evaluate the prognostic value of NLR and LMR before and after treatment in patients with HPV-negative HNSCC undergoing up-front surgical treatment. Methods: This multicentric retrospective study was performed on a consecutive cohort of patients treated by upfront surgery for HPV-negative HNSCC between April 2004 and June 2018. Only patients whose pre- and postoperative NLR and LMR were available were included. Their association with local, regional and distant failure, progression-free survival (PFS) and overall survival (OS) was calculated. Results: A total of 493 patients (mean age 68 years) were enrolled. The mean follow-up time was 54 months. Pre-surgical NLR ≥ 3.76 was associated with a high risk of regional failure (HR = 2.21, 95% CI: 1.08-5.55), disease progression (HR = 1.55, 95% CI: 1.07-2.25) and death (HR = 1.40, 95% CI: 0.94-2.10). A post-surgical LMR < 2.92 had a significant impact on disease progression (HR = 1.92, 95% CI: 1.13-3.28) and OS (HR = 2.98, 95% CI: 1.53-5.81). Patients with stable NLR ≥ 3.76 in the pre- and postoperative period had worse OS and PFS. Conclusions: Our results support that pre- and postoperative NLR and LMR can be useful in identifying patients at risk of local, regional, or distant recurrence who may require closer follow-up or more aggressive treatment.
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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 | 0.000 | 0.002 |
| 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".