Use of Benzodiazepines and Z-Drugs in Multiple Sclerosis
Bibliographic record
Abstract
Objective: Use of benzodiazepines and Z-drugs (non-benzodiazepine sedative hypnotics) is controversial due to adverse health outcomes in the general population. However, little is known about their use in people with multiple sclerosis (MS). We estimated the incidence and prevalence of benzodiazepine and Z-drug use (jointly BZD) in the MS population as compared to an age-, sex- and geographically-matched population without MS, and examined the association of mood/anxiety disorders with the use of BZD over a twenty-year period. Methods: Using administrative data from Manitoba, Canada, we identified 2,985 persons with incident MS and 14,891 persons without MS matched 5:1 on sex, birth year and region. We applied validated case definitions to identify persons with any mood/anxiety disorder. Dispensations of BZD were identified. To assess the association between MS, mood/anxiety disorders and BZD use we constructed generalized linear models adjusting for age, sex, index year, socioeconomic status, urban/rural residence, physical comorbidities, and health care use. We also examined patterns of BZD use. Results: In 2016, the crude incidence of benzodiazepine use in the MS cohort was 2.10% (95%CI: 1.43-2.98%), 1.49-fold higher than in the non-MS cohort (1.41%; 95%CI: 1.18-1.67%). The crude incidence of Z-drug use in the MS cohort was 1.77% (95%CI: 1.20-2.51%), 1.78-fold higher than in the non-MS cohort (0.99%; 95%CI: 0.81-1.21%). After adjusting for covariates, among individuals without an active mood/anxiety disorder, the MS cohort had a 39% increased incidence rate of benzodiazepine use and a 72% increased incidence rate of Z-drug use as compared to the non-MS cohort. Among individuals with an active mood/anxiety disorder, the incidence of BZD use did not differ between the MS and non-MS cohorts. A higher proportion of people with MS used BZD for ≥6 months than people without MS. Conclusion: Use of BZD is more common in people with MS than in general population controls, and use of these agents is in persons with MS is often chronic.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".