Multiple sclerosis disease-modifying drug use by immigrants: a real-world study
Bibliographic record
Abstract
Little is known about disease-modifying drug (DMD) initiation by immigrants with multiple sclerosis (MS) in countries with universal health coverage. We assessed the association between immigration status and DMD use within 5-years after the first MS-related healthcare encounter. Using health administrative data, we identified MS cases in British Columbia (BC), Canada. The index date was the first MS-related healthcare encounter (MS/demyelinating disease-related diagnosis or DMD prescription filled), and ranged from 01/January/1996 to 31/December/2012. Those included were ≥ 18 years old, BC residents for ≥ 1-year pre- and ≥ 5-years post-index date. Persons becoming permanent residents 1985-2012 were defined as immigrants, all others were long-term residents. The association between immigration status and any DMD prescription filled within 5-years post-index date (with the latest study end date being 31/December/2017) was assessed using logistic regression, reported as adjusted odds ratios (aORs) with 95% confidence intervals (CIs). We identified 8762 MS cases (522 were immigrants). Among immigrants of lower SES, odds of filling any DMD prescription were reduced, whereas they did not differ between immigrants and long-term residents across SES quintiles (aOR 0.96; 95%CI 0.78-1.19). Overall use (odds) of a first DMD within 5 years after the first MS-related encounter was associated with immigration status.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".