HPV Status Improves Classification of Head and Neck Gray Zone Cancers
Bibliographic record
Abstract
In epidemiologic studies, patients with head and neck squamous cell carcinoma (HNSCC) are classified mainly by the International Classification of Diseases (ICD) codes. However, some patients are of an unclear subsite, the "gray zone" cases, which could reflect ICD coding error, absence of primary subsite, or extensive primary tumors that cross over multiple subsites of the oral cavity and oropharynx. Patients with gray zone squamous cell carcinomas were compared with patients with oral cavity squamous cell carcinoma (OSCC) or oropharyngeal squamous cell carcinoma (OPSCC) and stratified by human papillomavirus (HPV) status that was determined by p16 immunostaining or HPV serology. Comparisons consisted of clinicodemographic features and prognostic outcomes presented by Kaplan-Meier curves and Cox proportional hazards regression models, reported as hazard ratios. There were 158 consecutive patients with gray zone HNSCC diagnosed at the Princess Margaret Cancer Center between 2006 and 2017: 66 had subsite coding discrepancies against the clinician's documentation ("discrepant" cases; e.g., the diagnosis by the clinician was OSCC, while the classification by ICD coding was OPSCC), while 92 were squamous cell carcinoma of unknown primary of the head and neck (SCCUPHN) after complete diagnostic workup. Comparators included 721 consecutive OSCC and 938 OPSCC adult cases. All HPV-positive cohorts (OPSCC, discrepant, and SCCUPHN) had similar clinicodemographic characteristics and better 3- and 5-y overall survival and disease-free survival than their HPV-negative counterparts. In contrast, HPV-negative discrepant cases had prognostic outcomes most similar to HPV-negative OPSCC cases, while HPV-negative SCCUPHN had survival outcomes most similar to those of patients with OSCC in this study. HPV-positive status can improve the classification of patients with unclear or discrepant oral/oropharyngeal subsite, an improvement over classification systems that are solely clinician defined or conducted through ICD coding. However, due to clinical practice, we could not make definitive reclassification for patients with HPV-negative gray zone HNSCC.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".