Predictors of circulating INTERLEUKIN-6 levels in head and neck cancer patients
Bibliographic record
Abstract
Circulating interleukin-6 (IL-6) improves outcome prediction for second primary cancer (SPC) in head and neck cancer (HNC) patients. This study aimed to identify factors associated with IL-6 serum levels in HNC patients. This study was conducted as part of a phase III chemoprevention trial. IL-6 was measured using chemiluminescent immunometric assay on pretreatment serum sample obtained from 527 stage I-II HNC patients. Patients’ lifestyle habits, sociodemographic, medical and tumor characteristics were evaluated before radiation therapy (RT). Factors independently associated with IL-6 levels before RT were identified using multiple linear regression. The median IL-6 serum level was 3.1 ng/L. In the multivariate analysis, eight factors were significantly associated (p < 0.05) with IL-6: age, gender, marital status, body mass index, tobacco consumption, comorbidities, Karnofsky Performance Status and HNC site. Smoking duration and lifetime pack-years were positively associated with IL-6 serum levels in a dose-response relationship (p-value for trend ≤0.03). Circulating IL-6 is a strong predictor of the occurrence of SPC in HNC patients. We identified eight factors independently associated with serum IL-6 levels in 527 stage I-II HNC patients. The dose-response relationship between lifetime smoking and IL-6 serum levels suggested a causal role of tobacco exposure on IL-6 production. Further studies are needed to establish whether the effect of tobacco exposure on SPC could be partly mediated by IL-6, a pro-inflammatory cytokine.
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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".