Cancer incidence and mortality rates in Bermuda.
Bibliographic record
Abstract
OBJECTIVE: To describe cancer and mortality rates in Bermuda and to compare such rates to those of the United States of America (U.S.A.). METHODS: Age-adjusted race-specific cancer incidence rates for Bermuda were calculated using the Bermuda Cancer Registry. These rates were then compared to U.S.A. cancer rates published by the National Cancer Institute. RESULTS: Overall age-adjusted incidence rate was 495 cases per 100,000 for Blacks and 527 cases per 100,000 for Whites. Incident cases were more frequent among men than women in both races. For Blacks, the highest incidences were prostate for men and breast for women, followed by colon/rectum and lung cancer. For Whites, if we exclude benign skin cancers, the picture was similar with the notable exception of lung cancer being more frequent than colon/rectum in White males. When Bermuda's rates were compared to those of the U.S.A., overall cancer rates were similar in both countries. Rates in Bermuda were higher for cancer of the mouth, ovarian cancer (Black women), melanoma (Whites), colorectal cancer (White women) and breast cancer (White women). Lung and colorectal cancers were less frequent in Bermuda's Black population. CONCLUSION: Further epidemiological studies are needed to identify potential risk factors that could contribute to these differences. Screening and prevention strategies could be adjusted accordingly.
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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 | 0.002 | 0.002 |
| 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) | 0.004 | 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".