Patterns of benzodiazepines use in primary care adults with anxiety disorders
Bibliographic record
Abstract
BACKGROUND: Benzodiazepines are among the most commonly prescribed drugs for anxiety disorders. While they are indicated as adjunctive treatment for short-term use according to clinical practice guidelines, previous studies have shown patterns of long-term use of benzodiazepines, which is problematic due to side effects, dependence and potential of abuse. The aims of this study were to examine among a large sample of primary care adults suffering from anxiety disorders: 1) benzodiazepine use patterns; and 2) correlates of long-term benzodiazepine use. METHODS: Data were drawn from the "Dialogue" project, a large primary care study conducted in 64 primary care clinics in the province of Quebec, Canada. Following a mental health screening in waiting rooms, patients at risk of anxiety or depression completed the Composite International Diagnostic Interview-Simplified (CIDIS). A sample of 740 adults meeting DSM-IV criteria for Generalized Anxiety Disorder, Panic Disorder or Social Anxiety Disorder in the past 12 months took part in this study. RESULTS: Benzodiazepines were used by 22.6% of participants with anxiety disorders in our primary care sample. A large majority of benzodiazepine users (88.4%) met our indicator of long-term use, as defined by utilization for more than 12 weeks including regular and as-needed use. Based on a logistic regression model, individual correlates associated with long-term benzodiazepine use included: being 30 years or older, having a comorbid physical illness, meeting criteria for comorbid agoraphobia, reporting the use of sleep-aids, and concurrent SSRI utilization. LIMITATION: Data collection with self-reported questionnaires may be subject to information bias. CONCLUSIONS: Despite knowledge of the risks of long-term use of benzodiazepines, this remains a pervasive problem. Clinicians need to be mindful of patterns and risk factors leading to long-term use of benzodiazepines in patients with anxiety disorders. Results of this study should raise awareness regarding appropriate prescription practices for benzodiazepines, including decision-making in initiation, duration of prescription, and use of strategies for discontinuation in current long-term benzodiazepine users.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".