Heart disease among Greenlandic children and young adults: a nationwide cohort study
Bibliographic record
Abstract
BACKGROUND: The incidences of heart disease (HD) and congenital heart disease (CHD) among Inuit in Greenland (GL) and Denmark (DK) are unknown. This study aims to estimate incidence rates (IRs) of HD and CHD among the young Inuit populations in Greenland and Denmark compared with rates among young non-Inuit populations in the same countries. METHODS: A register-based nationwide cohort including all individuals living in Greenland and Denmark from birth to age <40 years through 1989-2014 was formed. Ethnicity was considered Inuit/mixed if at least one parent was registered as being born in Greenland. Information on HD and CHD hospitalization was obtained from national inpatient registers using ICD-8 and ICD-10 codes. RESULTS: HD IR was lower among individuals living in Greenland compared with those living in Denmark, [73.35GL (95% confidence interval (CI) 68.07 to 79.03)] vs [88.07DK (95% CI 87.38 to 88.76)], whereas CHD IRs were almost similar in the two countries [IR 34.44GL (95% CI 30.89 to 38.40) vs IR 34.67DK (95% CI 34.24 to 35.10)]. Being of Inuit/mixed ethnicity was associated with an increased risk of both HD and CHD compared with non-Inuit in Greenland and Denmark [adjusted hazard ratio HD 2.07GL (95% CI 1.25 to 3.42)] and CHD [2.92GL (95% CI 1.34 to 6.38)]. CONCLUSION: HD IR was lower in individuals living in Greenland compared with individuals living in Denmark, whereas the CHD IRs were almost the same for both countries. However, the risk of HD including CHD was higher among individuals of Inuit/mixed ethnicity compared with non-Inuit in both countries, suggesting a role of ethnicity among children and younger adults.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".