Severe Neurological Disorders in the Greenlandic Population: A Nationwide Register‐Based Study
Bibliographic record
Abstract
ABSTRACT Background Few studies have addressed the burden of neurological disorders in Greenland (GL). We aim to estimate nationwide incidences and prevalence of a broad group of neurological disorders in the total Greenlandic population and according to ethnicity (Inuit, Non‐Inuit). To explore the importance of ethnic and environmental factors we estimated corresponding rates among Inuit and Non‐Inuit living in Denmark (DK). Methods A long‐standing collaboration between the Danish and Greenlandic healthcare system enabled us to follow the Greenlandic and Danish population for dementia, Parkinson's Disease (PD), epilepsy, stroke, and infections of the Central Nervous System using national registries from both countries. Incidence rates (IRs) were calculated using log linear Poisson‐regression for the combined period 1987‐2014, and stratified according to ethnicity, country of residence and periods. Age‐standardized IRs (ASIRs) were based on the WHO 2000–2025 standard population. Results The Greenlandic IRs of epilepsy and ischemic stroke were 98.6 (95% CI: 93.8–104) and 118 (95% CI: 113–124) respectively, per 100,000 person‐years of risk. IRs for the remaining neurological disorders were below 40 per 100,000. During follow‐up we observed an increase in IRs of ischemic stroke and a less pronounced for dementia. Apart from dementia, ASIRs of neurological disorders were generally higher in the Greenlandic population compared with the Danish, most pronounced for subarachnoid hemorrhage (ASIR GL /ASIR DK = 2.36 (2.12–2.62)). Inuit in Greenland were at a lower risk of stroke, PD and especially dementia (ASIR GL /ASIR DK = 0.40 (0.35–0.46)) compared with Inuit in Denmark. The most prevalent neurological disorders in Greenland in 2014 were stroke and epilepsy. Conclusion Our study emphasizes that stroke and epilepsy are important causes of morbidity in Greenland and suggests that dementia may become a challenge. Noticeable differences according to ethnicity and country of residence warrants further research.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".