Cancer in Greenlandic Inuit 1973–1997: A cohort study
Bibliographic record
Abstract
The increasing westernization of the Arctic countries may influence the very particular cancer profile of these populations. Our objective was to investigate the development in cancer incidence from 1973 to 1997 in a large and well-defined Inuit population in Greenland. Greenland is part of the Danish Kingdom, and population statistics covering both countries are available from the same registry resource. Data from the Danish Civil Registration System and from the Danish Cancer Registry were used to calculate age-standardized cancer incidence rates for the periods 1973-1987 and 1988-1997 for persons born in Greenland. Using rates for Denmark, sex-specific standardized incidence ratios (SIRs) for 1988-1997 were calculated. Furthermore, age- and sex-specific incidence rates in the 2 periods were calculated for selected cancers. Total cancer incidence increased from 248.5 to 277.9 per 100,000 person-years in men and from 269.4 to 302.2 per 100,000 person-years in women. The incidence of lung, stomach, breast and colon cancer increased, whereas the incidence of cervical cancer decreased. Compared to the Caucasian population in Denmark, high SIRs were found for cancers of the nasopharynx, salivary gland, esophagus, stomach and cervix and low SIRs for testis, bladder, prostate, breast and hematologic cancers. Overall cancer incidence among Greenlandic Inuit is increasing as a result of increases in several cancers that are common in Western populations. A significant increase in the incidence of stomach cancer in both sexes, which contrasts global trends for this cancer, warrants further investigation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".