Impact of HPV Status on the Prognostic Potential of the AJCC Staging System for Larynx Cancer
Bibliographic record
Abstract
Objective We evaluated the ability of the American Joint Committee on Cancer (AJCC) seventh edition staging system to prognosticate the overall survival of patients with human papillomavirus (HPV)-positive laryngeal squamous cell carcinoma. Study Design Retrospective analysis. Setting National Cancer Database. Subjects and Methods Patients diagnosed with laryngeal squamous cell carcinoma who were treated with curative intent were identified in the National Cancer Database. Multivariate analysis was utilized to determine factors correlated with overall survival in the HPV-negative and HPV-positive cohorts. Unadjusted and propensity score-weighted Kaplan-Meier estimation was used to determine overall survival of HPV-negative and HPV-positive patients across AJCC stage groupings. Results We identified 3238 patients with laryngeal squamous cell carcinoma, of which 2812 were HPV negative and 426 were HPV positive. Overall survival adjusted for age, sex, and comorbidity status confirmed significant differences among all consecutive stage groupings (I vs II, P < .001; II vs III, P < .05; III vs IVA, P < .001; IVA vs IVB, P < .05) in the HPV-negative cohort, whereas only stages IVAs and IVB ( P < .01) exhibited a significant difference in overall survival for HPV-positive patients. Conclusion The current AJCC staging system does not accurately distinguish risk of mortality for patients with HPV-positive disease. These data support the consideration of HPV status in estimating prognosis as well as clinical trial design and clinical decision making for patients with laryngeal squamous cell carcinoma.
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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 | 0.006 | 0.022 |
| 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 | 0.001 | 0.001 |
| 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".