Ethnic Differences in Survival for Female Cancers of the Breast, Cervix and Colorectum in British Columbia, Canada
Bibliographic record
Abstract
Background: Chinese and South Asians are among the fastest growing minority populations in Canada; however little is known about the burden of cancer in these populations. Objective:The objective is to examine survival rates for breast, cervical and colorectal cancers in women within these two ethnic populations, as compared to the BC general population. Methods: Survival rates were calculated for three time periods in the Chinese, South Asian and BC general populations, using the BC cancer registry. Ethnicity within the registry was determined using surnames. Results: Survival rates for female breast, cervical and colorectal cancers have improved over time in all three population groups, however general differences were found among the groups. Chinese women had higher survival rates than both South Asians and all BC women for breast and cervical cancer, and intermediate survival rates between South Asians and all BC women for colorectal cancer. South Asian women had the highest survival rates for colorectal cancer, similar survival rates to all BC women for breast cancer, and lower survival rates for cervical cancer. Interpretation: Differences in the observed survival rates may be explained by variations in screening and early detection, treatment practices, and cancer biology. This is discussed more fully for each cancer site.
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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 | 0.002 |
| 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) | 0.003 | 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".