Inflammatory Markers as Prognostic Factors of Recurrence in Advanced-Stage Squamous Cell Carcinoma of the Head and Neck
Bibliographic record
Abstract
Background: Multiple immunologic parameters have provided useful prognostic and assessment significance in various cancers, including head-and-neck squamous cell carcinoma (scc). We sought to identify whether pretreatment inflammatory markers could prognosticate recurrence in patients with advanced (stage iii or iv) head-and-neck scc who underwent therapy with curative intent in a tertiary care centre between January 2010 and December 2012. Methods: In a chart review, we recorded demographics; primary tumour characteristics; p16 status; pretreatment inflammatory markers, including body mass index (bmi), neutrophil-to-lymphocyte ratio (nlr), C-reactive protein (crp), and serum albumin; therapy received; and date of relapse, death, or last follow-up. The main outcome was relapse-free survival (rfs). Overall survival (os) was a secondary outcome. Results: From among 235 charts reviewed, 118 cases were included: 86 oropharyngeal (50 p16-positive, 18 p16-negative, 17 p16 unavailable, 1 p16 indeterminate), and 32 non-oropharyngeal (7 p16-positive, 19 p16-negative, 6 p16 unavailable). Median follow-up was 2.45 years (25%-75% interquartile range: 1.65-3.3 years). In univariate analysis, p16 status, bmi, modified Glasgow prognostic score, and crp were significant for rfs, but in multivariate analysis, only p16 status, bmi, and crp remained significant. For os, only crp and nlr were significant in both the univariate and multivariate analyses. After adjustment for p16 status, nlr did not remain significant. After adjustment for p16 status, crp remained significant for both rfs and os. Conclusions: In patients with head-and-neck scc, a stronger prognostic value is associated with human papillomavirus status than with nlr and many other factors, including bmi and albumin. However, even though few of our patients had high crp, serum crp remained significant despite p16-positive status.
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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".