Pharmacoepidemiology of Benzodiazepine and Sedative-Hypnotic Use in a Canadian General Population Cohort during 12 Years of Follow-up
Bibliographic record
Abstract
OBJECTIVE: benzodiazepines (BDZs) and similar sedative-hypnotics (SSHs) can have both beneficial and adverse effects. Clinical practice guidelines indicate that the course of treatment should usually be brief (a few weeks), but patients often take these medications for longer periods of time. We hypothesized that treatment with antidepressants (ADs) would be associated with a shorter duration of SSHs use as mood and anxiety disorders may underlie the symptoms usually targeted by BDZ treatment. METHOD: our study used data from a Canadian longitudinal general health study, the National Population Health Survey, which has collected data since 1994. Data are currently available to 2006. At each interview, all medications taken in the preceding 2 days are recorded. In our study, we used proportional hazard models to describe patterns of initiation and discontinuation of these medications in the general population. RESULTS: at each interview, the frequency of BDZ-SSH use was 2% to 3%. About 1% of the population initiated use in each 2-year follow-up period. Contrary to expectation, taking ADs predicted initiation of BDZ-SSHs, but not discontinuation. CONCLUSIONS: unexpectedly, respondents taking ADs had a higher frequency of new BDZ-SSH use. AD use may be a marker for depression severity or comorbidity, such that the observed results may be an artifact of confounding by these factors. Irrespective of etiology, initiation of AD treatment does not appear to negate the risk of long-term BDZ-SSH use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".