Use of Benzodiazepines and Z-Drugs in Inflammatory Bowel Disease
Bibliographic record
Abstract
INTRODUCTION: We estimated the incidence and prevalence of benzodiazepine and Z-drug (separately and jointly as BZD) use in the inflammatory bowel disease (IBD) population compared with matched controls without IBD and examined the association of mood/anxiety disorders (M/ADs) with the use of BZD from 1997 to 2017. METHODS: Using administrative data from Manitoba, Canada, we identified 5,741 persons with incident IBD who were matched in a 1:5 ratio to controls on sex, birth year, and region. Validated case definitions were used to identify M/AD. Dispensations of BZD were identified. Multivariable generalized linear models were used to assess the association between IBD, M/AD, and BZD use. RESULTS: In 2016, the incident age/sex-standardized benzodiazepine use rates per 1,000 were 28.06 (95% confidence interval [CI] 26.41-29.81) in the IBD cohort and 16.83 (95% CI 16.28-17.39) in controls (adjusted rate ratio = 1.69 [95% CI 1.56-1.79]). Benzodiazepine incidence rates were higher for women with IBD than men, but the RR between cases and controls were similar for men and women. The incident age/sex-standardized Z-drug use rate per 1,000 was 21.07 (95% CI 19.69-22.41) in the IBD cohort. This was 1.87-fold higher than in controls (95% CI 1.73-2.01). In 2017, approximately 20% of persons with IBD used benzodiazepines and 20% used Z-drugs. There was a subadditive effect of both benzodiazepine and Z-drug uses between IBD and M/AD after adjusting for covariates. DISCUSSION: The use of BZD is more common in people with IBD than in population controls. Strategies to reduce the use of BZDs in persons with IBD and to offer alternative management strategies for M/ADs, sleep disorders, and other symptomatic concerns are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".