Socioeconomic contributions to clinical outcome by HPV status in squamous cell carcinomas of the head and neck (SCCHN): An analysis of NCDB.
Bibliographic record
Abstract
e18214 Background: HPV association remains one of the most important predictors of clinical outcome in SCCHN. We aimed to determine whether the relationship between HPV status and overall survival (OS) varied by certain socioeconomic factors. Methods: Data were obtained from the National Cancer Database (NCDB). We examined the relationship between OS and HPV status, controlling for demographics and socioeconomic variables (insurance, income, education, urban/rural, great circle distance, and/or distance to treatment facility of 0-10, 10-50, and > 50 miles). Results: HPV status modified the relationship between insurance status (p = 0.011), urban/rural residence (p = 0.041), and overall survival, controlling for age, race, sex, and clinical stage. Whereas for HPV- patients, government insurance conferred a lower risk of death compared to no insurance (HR: 0.86, 95% CI: 0.75-0.99, p = 0.038); this finding did not hold for patients with HPV related disease (HR: 0.97, 95% CI: 0.80-1.18, p = 0.781). For patients with HPV related SCCHN, those living in rural areas have significantly higher risk of death compared to those living in metro areas (HR: 1.55, 95% CI: 1.17-2.05, p = 0.002), which did not hold for patients with HPV unrelated SCCHN (HR: 0.94, 95% CI: 0.68-1.30, p = 0.703). Conclusions: Despite HPV’s prospective prognostication, patients with HPV + SCCHN who live in rural environments have a higher risk of death likely from lack of physical access to care. This finding did not hold for HPV negative population, presumably because of their worse outcomes at baseline, and their struggle for access to care regardless of physical location.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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, 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".