Polypharmacy and multiple sclerosis: A population-based study
Bibliographic record
Abstract
BACKGROUND: Little is known about polypharmacy and multiple sclerosis (MS). OBJECTIVES: To estimate polypharmacy prevalence in a population-based MS cohort and compare persons with/without polypharmacy. METHODS: Using administrative and pharmacy data from Canada, we estimated polypharmacy prevalence (⩾5 concurrent medications for >30 consecutive days) in MS individuals in 2017. We compared the characteristics of persons with/without polypharmacy and described the number of polypharmacy days, the most common medication classes contributing to polypharmacy and hyper-polypharmacy prevalence (⩾10 medications). RESULTS: = 3995) met criteria for polypharmacy (median polypharmacy days = 273 (interquartile range (IQR): 120-345)). Odds of polypharmacy were higher for women (adjusted odds ratio (aOR) = 1.14; 95% confidence intervals (CI):1.04-1.25), older individuals (aORs 50-64 years = 2.04; 95% CI:1.84-2.26; ⩾65 years = 3.26; 95% CI: 2.92-3.63 vs. <50 years), those with more comorbidities (e.g. ⩾3 vs. none, aOR = 6.03; 95% CI: 5.05-7.22) and lower socioeconomic status (SES) (e.g. most (SES-Q1) vs. least deprived (SES-Q5) aOR = 1.64; 95% CI: 1.44-1.86). Medication classes most commonly contributing to polypharmacy were as follows: antidepressants (66% of polypharmacy days), antiepileptics (47%), and peptic ulcer drugs (41%). Antidepressants were most frequently co-prescribed with antiepileptics (34% of polypharmacy days) and peptic ulcer drugs (27%). Five percent of persons (716/14,227) experienced hyper-polypharmacy. CONCLUSION: More than one in four MS persons met criteria for polypharmacy. The odds of polypharmacy were higher for women, older persons, and those with more comorbidities, but lower SES.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".