Characteristics of persons with multiple sclerosis covered by public drug insurance in a Quebec Birth Cohort
Bibliographic record
Abstract
Objective: People living with multiple sclerosis use medications for several indications, but little is known about their prescription drug use in Quebec, notably because the public drug insurance covers only part of the population. We compared the characteristics of those with public drug insurance to those privately covered. Methods: In a cohort of persons born in 1970-1974, we identified those living with multiple sclerosis by applying a validated algorithm to administrative health data. Individuals with public coverage were those who had at least one covered period after their date of diagnosis between January 1, 1997, and December 31, 2014. We used descriptive statistics to compare sociodemographic and healthcare utilization characteristics by type of coverage. Results: Among the 1363 persons living with multiple sclerosis, 720 (53 %) were covered by the public drug insurance. Individuals with public drug coverage were younger, more likely to be materially and socially deprived, and had a lower median income than those with private insurance, but otherwise had similar sociodemographic characteristics. The proportion of people who had at least one multiple sclerosis-related visit to a general practitioner (39 % versus 45 %) and hospitalization (6 % versus 3 %) differed among those with public compared to private coverage. However, the utilization of other health services, including neurologist consultations, did not differ by type of drug coverage. Conclusion: People with multiple sclerosis covered by the public and private drug insurance differed in terms of age, income, deprivation, multiple sclerosis-related visits to a general practitioner and hospitalizations, but not neurologist consultations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".