Cancer incidence and mortality in Greenland 1983-2014, including comparison with the other Nordic countries.
Bibliographic record
Abstract
e13590 Background: During the last decades social and life-style changes have altered the disease pattern in Greenland with an increase in the incidence of several life-style related diseases. Our aim is to present the incidence and mortality of cancer in Greenland and compare the results with the other Nordic countries. Methods: The data is obtained from The Danish Cancer Registry and The Danish Register of Causes of Death. Comparable data on cancer incidence and mortality in Denmark, Finland, Iceland, Norway, Sweden and Greenland are available for analysis through a collaboration of The Nordic Cancer Registries (NORDCAN). We included all patients living in Greenland and diagnosed with a cancer during 1983 to 2014. Results: The total number of cancers for the study period was 4,716 (2,362 among women and 2,354 among men). The cancer incidence was highest for gastrointestinal cancer and respiratory cancer for both sexes. Furthermore the incidence of cancer of the female genital organs and breast cancer was high among women, whereas the incidence of cancer of the lip, oral cavity and pharynx was high among men. Notably higher incidence rates of cancer of the lip, oral cavity and pharynx, respiratory cancer, and cancer of unknown sites were seen compared to the other Nordic countries. The incidence rates of breast cancer and cancer of male genital organs were lower than the other Nordic countries. Cancer-specific mortality rates were much higher in Greenland compared to the other Nordic countries. The time trend from 1983 to 2014 showed a significant increase in cancer incidence in Greenland, but with no change in the cancer-specific mortality. Conclusions: The trends in cancer incidence and mortality in Greenland compared to the other Nordic countries have not been reported earlier. These data underline a need to focus on cancer-specific mortality in Greenland and prevention of high-incidence cancers related to well-established risk factors. Disparities in cancer incidence and mortality are also seen among other Inuit communities and indigenous people in general. Enhanced attention to indigenous people to ensure high-quality and high-value cancer care is necessary for a better worldwide cancer control.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".