Abstract 4676: Survival of gastric cancer differs by ethnicity: A population-based experience from British Columbia, Canada
Bibliographic record
Abstract
Abstract Introduction: Prognostic factors can be classified into three broad groups: i) tumor-related, ii) host-related, and iii) environment-related (health care, treatment, lifestyle) factors. The current study was conducted to examine the effect of ethnicity as a host-related factor for survival of gastric cancer patients in British Columbia, Canada. Methods: Data were obtained from the population-based BC Cancer Registry for patients diagnosed with invasive gastric cancer between 1984 and 2006. The ethnicity of patients was estimated according to their names and categorized as Chinese, South Asian, Iranian or Other (more than 80% of “Other” are British and Western Europeans). Cox proportional hazards regression analysis was used to estimate the effect of ethnicity adjusted for patient sex and age, disease histology, tumor location, disease stage and treatment. Results: Results from this study indicate significantly different survival of gastric cancer patients depending on their ethnic group (p<0.01). When considered separately according to the presence or absence of metastatic disease, significant differences were only found for non-metastatic disease (p<0.01). Furthermore, the association between survival and ethnicity was only significant for patients with non-metastatic disease who received therapeutic surgery (p<0.01). In multivariate analyses adjusting for patient factors, disease factors and treatment, there was a significant difference among ethnic groups. Only Chinese had significantly longer survival compared to the Other ethnicities, but this survival advantage was only seen for non-metastatic disease (HR=0.78, 95% CI=0.64-0.95). Conclusion: The difference observed in patient survival is not likely to be due to healthcare disparities among minority groups, as all BC residents are covered for healthcare through the BC Medical Services Plan (MSP). Ethnicity may represent underlying genetic factors. Such factors could influence host-tumor interactions by altering the tumor's etiology and therefore its chance of spreading. Alternatively, genetic factors may determine response to treatments. Finally, ethnicity may represent non-genetic factors that affect survival. Differences in survival support the importance of ethnicity as a prognostic factor, and may provide clues for the future identification of genetic or lifestyle factors that underlie these observations. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4676. doi:10.1158/1538-7445.AM2011-4676
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".