Multiple Sclerosis in the Greenlandic population. A nationwide cohort study
Bibliographic record
Abstract
BACKGROUND: The Global Burden of Disease Group modelled a surprisingly high prevalence of Multiple Sclerosis (MS) in the Greenlandic population in 2016 of 290 per 100,000, higher than that in Denmark, a high-risk country of MS. OBJECTIVE: Obtain accurate data on the burden (prevalence and incidence rates) of MS in Greenland, 1973 - 2014, overall and according to ethnicity. METHODS: Greenlandic prevalence and incidence rates were estimated using validated data from the Danish Multiple Sclerosis Registry (Greenlandic citizens suspected of MS were until 2014 supposed to be transferred to Denmark). The relative risk of MS in the Greenlandic population versus that in the Danish was estimated as the ratio of observed to expected numbers of MS according to Danish national sex-, age- and period-specific MS incidence rates, i.e. Standardized Incidence Ratio (SIR). RESULTS: In 2014, seven persons were living with MS in Greenland (prevalence=12.52 per 100,000) and 22 new MS cases were registered from 1973 to 2014 (incidence rate=0.97 (95 % CI; 0.64-1.47) per 100,000 person-years of risk). Inuit and Non-Inuit in Greenland, and Inuit in Denmark had MS risks of 2 % (SIR=0.02 (0.01-0.06)), 67 % (SIR=0.67 (0.42-1.04)) and 23 % (SIR=0.23 (0.13-0.39)) respectively, of that in the Danish Non-Inuit population. To enhance completeness, possibly at the risk of misclassification we included MS diagnoses from the Greenlandic and Danish National Patient registries. In 2014, prevalence of MS in Greenland was now 48.29 per 100,000. CONCLUSION: Our study does not support prior claims of Greenland being a high MS risk country. MS is still relatively rare among Inuit living in Greenland.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".