Multiple Sclerosis in Newfoundland and Labrador - A Model for Disease Prevalence
Bibliographic record
Abstract
BACKGROUND: Newfoundland and Labrador, Canada, have been almost exclusively populated by immigrants from southwest England and southeast Ireland. The province's population grew largely by natural increase from 20,000 people in 1835 to half a million at present. Very little interregional migration occurred within the province. This uniquely-populated region and its subsequent founder effect provide the basis to develop models of disease prevalence. OBJECTIVES: To develop a model for the regional prevalence of multiple sclerosis (MS), accounting for settlement patterns and geographic location (latitude). METHODS: All living MS patients with confirmed addresses (438 patients) in the province were mailed a survey requesting their place of birth. Regional prevalences were calculated from a 75% rate of return of the survey. Theoretical regional prevalences were proportionally calculated from the source prevalences of southwest England, southeast Ireland, Scotland and the Channel Islands based on settlement patterns. These theoretical regional prevalences were corrected for geographical variations of latitude based on observations in the United Kingdom. Theoretical and actual regional prevalences were compared. RESULTS: When actual regional prevalences were compared with theoretical prevalences, very little variation was noted, especially after correcting for variation in latitude. CONCLUSION: A regional variation in MS prevalence is noted in the island portion of Newfoundland and Labrador. This regional variation can be modeled by using both migration patterns and latitudinal position. This model demonstrates that the prevalence of MS is influenced by both genetic and environmental contributions.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".