CNS infections in Greenland: A nationwide register-based cohort study
Bibliographic record
Abstract
BACKGROUND: Indigenous Arctic people suffer from high rates of infectious diseases. However, the burden of central nervous system (CNS) infections is poorly documented. This study aimed to estimate incidence rates and mortality of CNS infections among Inuits and non-Inuits in Greenland and in Denmark. METHODS: We conducted a nationwide cohort study using the populations of Greenland and Denmark 1990-2012. Information on CNS infection hospitalizations and pathogens was retrieved from national registries and laboratories. Incidence rates were estimated as cases per 100,000 person-years. Incidence rate ratios were calculated using log-linear Poisson-regression. Mortality was estimated using Kaplan-Meier curves and Log Rank test. RESULTS: The incidence rate of CNS infections was twice as high in Greenland (35.6 per 100,000 person years) as in Denmark (17.7 per 100,000 person years), but equally high among Inuits in Greenland and Denmark (38.2 and 35.4, respectively). Mortality from CNS infections was 2 fold higher among Inuits (10.5%) than among non-Inuits (4.8%) with a fivefold higher case fatality rate in Inuit toddlers. CONCLUSION: Overall, Inuits living in Greenland and Denmark suffer from twice the rate of CNS infections compared with non-Inuits, and Inuit toddlers carried the highest risk of mortality. Further studies regarding risk factors such as genetic susceptibility, life style and socioeconomic factors are warranted.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".