Postoperative Radiotherapy ± Cetuximab for Intermediate-Risk Head and Neck Cancer
Bibliographic record
Abstract
PURPOSE Radiotherapy (RT)/cetuximab (C) demonstrated superiority over RT alone for locally advanced squamous head and neck cancer. We tested this in completely resected, intermediate-risk cancer. METHODS Patients had squamous cell carcinoma of the head and neck (SCCHN) of the oral cavity, oropharynx, or larynx, with one or more risk factors warranting postoperative RT. Patients were randomly assigned 1:1 to intensity-modulated RT (60-66 Gy) with once-per-week C or RT alone. The primary hypothesis was that RT + C would improve overall survival (OS) in randomly assigned/eligible patients, with a prespecified secondary plan to test this in the human papillomavirus (HPV)–negative subpopulation. Disease-free survival (DFS) and toxicity were secondary end points. OS and DFS were tested via stratified log-rank test; toxicity was compared via Fisher's exact test. RESULTS We enrolled 702 patients from November 2009 to March 2018; 577 were randomly assigned/eligible. Most (63.6%) had oral cavity cancer and most (84.6%) had high epidermal growth factor receptor expression. There were fewer deaths (184) than expected. OS (median follow up, 7.2 years) was not significantly improved (hazard ratio [HR], 0.81; one-sided P = .0747; 5-year OS 76.5% v 68.7%), but DFS was (HR, 0.75; one-sided P = .0168; 5-year DFS 71.7% v 63.6%). Benefit of RT + C was only seen in the HPV-negative subpopulation (80.2% of patients in the trial). Grade 3-4 acute toxicity rates were 70.3% (RT + C) versus 39.7% (RT; two-sided P < .0001), mostly skin and/or mucosal effects. Late grade ≥3 toxicity rate was 33.2% (RT + C) versus 29.0% (RT; two-sided P = .3101). There were no grade 5 toxicities in either arm. CONCLUSION RT + C significantly improved DFS, but not OS, with no increase in long-term toxicity, compared with RT alone for resected, intermediate-risk SCCHN. RT + C is an appropriate option for carefully selected patients with HPV-negative disease.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Non-randomized trial | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.001 |
| 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) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".