Abstract A69: Cancer incidence rates for South Asians and Chinese living in British Columbia
Bibliographic record
Abstract
Introduction: South Asian and Chinese are the two largest minority groups in Canada with over 1,000,000 people each. In British Columbia these two ethnic populations account for about 70% of the province9s visible minority. While ethnic minorities represent a large segment of our population there is minimal data characterizing their cancer experience hindering the planning and implementation of effective national cancer control strategies. Objectives: The study objectives were to determine incidence rates for oral, respiratory and gastro-intestinal cancers among BC South Asians and BC Chinese and compare these rates to the rest of BC population. Methods: All new invasive cancers diagnosed between January 1, 1990 and December 31, 1999 were extracted from the BC Cancer Registry. Cases were classified as BC South Asian or BC Chinese by using surnames lists compiled from local telephone directories and the BC Screening Mammography Program. Population counts were extracted from the 1996 Census. Age standardized incidence rates were calculated using as standard the 1996 BC general population. Results: The highest incidence of nasopharyngeal, liver and stomach cancers was found among Chinese. Incidence rates for nasopharyngeal cancer were 11.24/100,00 population for Chinese males, 0.59/100,000 for BC males and 0.16 for South Asian males. Among south Asians the highest incidence rates were for cancer of the mouth and gallbladder. Incidence rate of gallbladder among South Asian women was 4 times the BC and Chinese women rates. Colorectal cancer incidence rates were highest among BC population. Conclusions: The data show cancer incidence rates that are distinctive for each population underscoring the importance of reporting cancer rates by ethnic populations and the need to tailor prevention strategies to each of them.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".