Asian and non-Asian disparities in outcomes of head and neck cancer (HNC).
Bibliographic record
Abstract
6567 Background: Racial differences in cancer outcomes are frequently observed for specific tumor types, including nasopharyngeal cancers, but prior research has mainly focused on disparities between Black and White races. Our aim was to evaluate the impact of Asian and non-Asian races on overall survival (OS) in a large population-based cohort of HNC. Methods: All patients diagnosed with non-nasopharyngeal HNC from 2001 to 2010 and referred to any 1 of 5 regional comprehensive cancer centers in British Columbia, Canada were reviewed. Using specialized software (Onomap, Inc.) that recognized common and distinctive surnames based on race, patients were classified as Asians vs. non-Asians. Using Kaplan-Meier methods and Cox regression, we examined the relationship between race and OS while controlling for confounders that consisted of additional socio-demographics and other tumor and treatment-related characteristics. Results: We identified a total of 3,036 patients: median age was 64 years (range 20-100), 74% were men, 32% were ECOG 0/1, and 7% and 93% were Asian and non-Asian, respectively. Comparing baseline characteristics between racial groups, Asians tended to exhibit worse prognostic features in that they had poorer functional status (ECOG 2+, 29% vs. 23%, p=0.07) and were more frequently affected by larger tumors (>4 cm, 33% vs 21%, p=0.02) and by oral cavity cancers (38% vs. 25%, p<0.001) than non-Asians. With respect to treatment, Asians were less likely to receive multimodality therapy than non-Asians (90% vs. 95%, p=0.02). Upon adjusting for prognostic factors, multivariate models showed that non-Asians actually had significantly higher odds of death when compared to Asians (HR 2.46, 95%CI 1.25-4.87, p=0.009). Advanced age, worse ECOG, greater tumor size, and lack of treatment also correlated with inferior OS. Conclusions: In addition to the racial differences reported in the literature for nasopharyngeal carcinoma, we observed variations in non-nasopharyngeal HNC outcomes between Asians and non-Asians. Despite worse prognostic features and less treatment, Asians exhibited better survival than non-Asians, suggesting a potential difference in tumor biology, pharmacogenetics, or predisposition to HPV exposure.
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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 | 0.001 |
| 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) | 0.003 | 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".