Serum Prognostic Markers in Head and Neck Cancer
Bibliographic record
Abstract
PURPOSE: Recognized prognostic factors do not adequately predict outcomes of head and neck cancer (HNC) patients after their initial treatment. We identified from the literature nine potential serum prognostic markers and assessed whether they improve outcome prediction. EXPERIMENTAL DESIGN: A pretreatment serum sample was obtained from 527 of the 540 HNC patients who participated in a randomized controlled trial. During follow-up, 115 had a HNC recurrence, 110 had a second primary cancer (SPC), and 216 died. We measured nine potential serum prognostic markers: prolactin, soluble interleukin-2 (IL-2) receptor-alpha, vascular endothelial growth factor, IL-6, squamous cell carcinoma antigen, free beta-human choriogonadotropin, insulin-like growth factor-I, insulin-like growth factor binding protein-3, and soluble epidermal growth factor receptor. Cox regression was used to identify a reference predictive model for (a) HNC recurrence, (b) SPC incidence, and (c) overall mortality. Each serum marker was added in turn to these reference models to determine by the likelihood ratio test whether it significantly improved outcome prediction. We controlled for the false discovery rate that results from multiple testing. RESULTS: IL-6 was the only serum marker that significantly improved outcome prediction. Higher levels of IL-6 were associated with a higher SPC incidence. The hazard ratio comparing the uppermost quartile to the lowest quartile of IL-6 was 2.68 (95% confidence interval, 1.49-4.08). IL-6 was also associated with SPC-specific mortality but not with mortality due to other causes. No marker improved outcome prediction for cancer recurrence or overall mortality. CONCLUSIONS: IL-6 significantly improves outcome prediction for SPC in HNC patients.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".