The association of ethnicity and oncologic outcomes for oral cavity squamous cell carcinoma (OSCC)
Bibliographic record
Abstract
Abstract Objective To compare oncologic outcomes of South Asian (SA) patients treated at a regional cancer centre in Canada, for oral squamous cell carcinoma (OSCC), to the general population. Methods Adult patients who underwent primary surgical resection of OSCC +/- adjuvant treatment between 2009 and 2022 (N=697) were included. SA patients were identified using a validated method and compared to non-SA patients. Baseline characteristics, including betel nut consumption, were compared, and disease-specific survival (DSS) and recurrence-free survival (RFS) were evaluated using Kaplan-Meier methods, with median follow-up time of 36.4 months [SD 31.02]. Cox proportional hazard regression models adjusted for potential confounders. A p-value < 0.05 was considered statistically significant. Results SA patients (9% of cohort, n = 64) were significantly younger and had lower rates of smoking and alcohol consumption compared to non-SA patients. There were no differences in tumor characteristics or the use of adjuvant radiation. SA patients had a two-fold higher risk of recurrence and significantly worse disease-specific survival, even after adjusting for stage and high-risk features [RFS: HR 2.01(1.28 - 3.14), DSS: HR 1.79(1.12 - 2.88)]. The consumption of betel nut was not associated with outcomes. Conclusion To our knowledge, this study is the first to compare the oncological outcomes of SA patients with OSCC to non-SA patients. SA patients had significantly worse outcomes, even after controlling for known predictors of recurrence and disease-specific survival. These findings can inform personalized treatment decisions and influence public health policies when managing patients with different ethnic backgrounds.
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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 | 0.002 |
| 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) | 0.002 | 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".