Prevalence and predictive role of p16 and epidermal growth factor receptor in surgically treated oropharyngeal and oral cavity cancer
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to describe the relationship of p16 and epidermal growth factor receptor (EGFR) expression with survival in surgically treated patients who had oropharyngeal or oral cavity squamous cell carcinoma (SCC). METHODS: Tissue from 36 patients with oropharyngeal SCC and 49 patients with oral cavity SCC treated between 1997 and 2001 was imbedded and immunostained using a tissue microarray. RESULTS: The p16 was positive in 57% and 13% of patients with oropharyngeal SCC and oral cavity SCC, respectively. EGFR was positive in 60% and 63% of patients with oropharyngeal SCC and oral cavity SCC, respectively. In patients with oropharyngeal SCC, p16 expression was associated with improved disease-specific survival (DSS), overall survival (OS), and time to recurrence (TTR) (p < .01, < .01, and <.01, respectively). EGFR expression was associated with poorer DSS, OS, and TTR (p < .01, = .01, and < .01, respectively). For oropharyngeal SCC, when examining both p16 and EGFR expression as combined biomarkers, high p16 expression coupled with low EGFR expression was associated with improved DSS (p p16 = .01; p EGFR = .01). Patients with oral cavity SCC showed no association between biomarker and outcome. CONCLUSIONS: For patients with oropharyngeal SCC, high p16 and low EGFR were associated with improved outcome, suggesting a predictive role in surgically treated 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 | 0.000 | 0.001 |
| 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".