High prevalence of comorbidities at diagnosis in immigrants with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) has been associated with certain comorbidities in general population studies, but it is unknown how comorbidity may affect immigrants with MS. OBJECTIVE: To compare prevalence of comorbidities in immigrants and long-term residents at MS diagnosis, and in matched control populations without MS. METHODS: We identified incident MS cases using a validated definition applied to health administrative data in Ontario, Canada, from 1994 to 2017, and categorized them as immigrants or long-term residents. Immigrants and long-term residents without MS (controls) were matched to MS cases 3:1 on sex, age, and geography. RESULTS: There were 1534 immigrants and 23,731 long-term residents with MS matched with 4585 and 71,193 controls, respectively. Chronic obstructive pulmonary disease (COPD), diabetes, hypertension, ischemic heart disease, migraine, epilepsy, mood/anxiety disorders, schizophrenia, inflammatory bowel disease (IBD), and rheumatoid arthritis were significantly more prevalent among immigrants with MS compared to their controls. Prevalence of these conditions was generally similar comparing immigrants to long-term residents with MS, although COPD, epilepsy, IBD, and mood/anxiety disorders were less prevalent in immigrants. CONCLUSION: Immigrants have a high prevalence of multiple comorbidities at MS diagnosis despite the "healthy immigrant effect." Clinicians should pay close attention to identification and management of comorbidity in immigrants with MS.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".