Bibliographic record
Abstract
Objective To measure the incidence of long-term benzodiazepine receptor agonist (BZRA) use among individuals with anxiety, mood and/or sleep disorders. To identify factors associated with long-term use following the first prescription. Methods This was a population-based retrospective cohort study using administrative databases in Manitoba, Canada. Individuals with anxiety/mood or sleep disorder who received their first BZRA between 1 April 2001 and 31 March 2015 were included. Long-term use was defined as ≥180 days. Logistic regression modelling was used to examine predictors of long-term use. Results Among 206 933 individuals included, long-term BZRA use in the first episode of use was 4.5% (≥180 days) following their first prescription. Factors associated with ≥180 days of use included male sex (adjusted OR (aOR) 1.33, 95% CI 1.27 to 1.39), age ≥65 (aOR 5.15, 95% CI 4.81 to 5.52), income assistance (aOR 1.68, 95% CI 1.55 to 1.81), previous non-BZRA psychotropic (aOR 1.93, 95% CI 1.83 to 2.02) or opioid use (aOR 1.16, 95% CI 1.11 to 1.22), high comorbidity (aOR 1.43, 95% CI 1.32 to 1.55), high healthcare use (aOR 1.46, 95% CI 1.33 to 1.60) and psychiatrist prescriber (aOR 2.11, 95% CI 1.93 to 2.32). Conclusions Less than 1 in 20 patients use BZRAs ≥180 days in their first treatment episode. Several factors were associated with long-term use following the first prescription and further investigation into whether these factors need to be considered at the point of prescribing is warranted. In light of these findings, future research should examine the predictors of cumulative repeat episodes of BZRA exposure.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.053 | 0.012 |
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".