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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".