Bibliographic record
Abstract
BACKGROUND: Inuit people are known to be at an increased risk of cancers usually uncommon to the western world such as cancers of the nasopharynx and salivary glands. But what is the trend regarding pancreatic cancer? OBJECTIVE: To determine the incidence of pancreatic cancer (PC) in Greenland compared with Denmark in the period 2000-2010. STUDY DESIGN: Retrospective register-based study. Cases were retrieved from The Danish Cancer Register and The Greenlandic Patient Register and stratified in 5-year age intervals for each year. Age-standardized incidence ratios (SIR) for each year for Greenland compared with Denmark were calculated using the number of cases and the number of inhabitants in each 5-year age interval and in each country. The average SIR for the entire period was calculated using a weighted average. RESULTS: The study revealed a SIR of 2.38 (95% CI: 1.97-2.86; p<0.0001) indicating a significantly increased incidence of PC in Greenland compared with Denmark. A linear regression analysis showed no significant change in the SIR over time (p for trend 0.25) as well as no significant change isolated in Greenland (p for trend 0.8). Furthermore, the Inuit were significantly younger at the time of diagnosis (mean 62.7 vs. 70.0; p<0.0001). CONCLUSIONS: The age-standardized incidence of PC is 138% higher in Greenland than in Denmark. A part of this could be explained by a higher prevalence of smoking and DM-2. However, the impact of genetic factors cannot be disregarded and should be subjected to 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".