Asian and non‐Asian disparities in outcomes of non‐nasopharyngeal head and neck cancer
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To evaluate disparities in overall survival (OS) between Asian and non-Asian patients diagnosed with non-nasopharyngeal head and neck cancer (HNC). STUDY DESIGN: This was a population-based, retrospective study of patients diagnosed with non-nasopharyngeal HNC of squamous cell carcinoma histology between 2001 and 2010 in British Columbia, Canada. METHODS: Using Kaplan-Meier methods and Cox regression models, we examined the relationship between race and OS. RESULTS: A total of 3,036 patients were included in the study. Median age was 64 years, 74% were men, and 7% were Asians. Asians had worse Eastern Cooperative Oncology Group (ECOG) status (29% vs. 23%, P = .07) and larger tumors (33% vs. 21%, P = .02), and were more likely to be diagnosed with oral cavity cancers (38% vs. 25%, P < .001) than non-Asians. Asians were also less likely to receive multimodality therapy than non-Asians (90% vs. 95%, P = .02). Asians were more likely to have never smoked (49% vs. 15%, P < .001) and to be married or with a partner (80% vs. 69%, P = .02). Multivariate models showed that Asians had better OS than non-Asians (hazard ratio [HR] = 0.50, 95% confidence interval [CI] = 0.25-0.99, P = .05). Three-year OS did not differ significantly between Asians and non-Asians (41% vs. 42%, P = .18); however, 5-year OS did (22% vs. 19% P = .03). Stratifying by treatment type, outcomes were comparable in both groups except for radiotherapy alone, where Asians showed significantly better OS (HR = 0.71, 95% CI = 0.51-0.99, P = .04). Advanced age, worse ECOG, greater tumor size, and lack of treatment also correlated with inferior OS. CONCLUSIONS: Despite several worse prognostic features and less aggressive treatment, Asians tended to exhibit better OS than non-Asians. LEVEL OF EVIDENCE: 2c. Laryngoscope, 127:2528-2533, 2017.
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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 | 0.001 |
| 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) | 0.002 | 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".