Abstract B76: The effect of ethnicity on esophageal cancer survival in British Columbia, Canada
Bibliographic record
Abstract
Abstract Background: Esophageal cancer is one of the most lethal human malignancies. This study compares the survival of esophageal cancer patients among ethnic groups in British Columbia (BC), specifically Chinese, South Asians and Iranians. Methods: Data was obtained from the population-based BC Cancer Registry for patients diagnosed with invasive esophageal cancer between 1984 and 2006. Complete follow-up information was available for all patients to 31 August 2007. The ethnicity of patients was determined according to their names and categorized as Chinese, South Asian, Iranian or Other. Cox proportional hazards regression analysis was used to estimate the effect of ethnicity adjusted for patient gender, patient age, disease histology, tumor location, disease stage and treatment. Results: The survival of esophageal cancer patients was significantly different among ethnic groups when evaluated without adjusting for other variables (p=0.03), but not in the multivariate model (p=0.06). A significant survival difference was observed among ethnic groups only for non-metastatic disease (p=0.049). The results indicate that South Asians have better survival than the other groups. Discussion: Ethnicity may represent underlying genetic factors that could affect survival. Such factors could influence host-tumor interactions by altering the tumor's etiology and therefore its chance of spreading. Host pharmacogenetic factors can also affect a patient's response to treatment. Differences in survival by ethnicity 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 differences. Citation Information: Cancer Epidemiol Biomarkers Prev 2010;19(10 Suppl):B76.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".