Coprescribed Benzodiazepines in Older Adults Receiving Antidepressants for Anxiety and Depressive Disorders
Bibliographic record
Abstract
OBJECTIVE: There is a paucity of data on the effects of coprescribed benzodiazepines on treatment response variability and adherence to antidepressant pharmacotherapy for depression and anxiety in late life. The objective of this transdiagnostic analysis was to examine the effect of benzodiazepines on treatment outcomes in older patients with generalized anxiety disorder (GAD) or major depressive disorder (MDD). METHODS: Secondary analyses of data from 2 clinical trials of antidepressant pharmacotherapy for GAD (escitalopram vs placebo, 2006-2009) or MDD (open treatment with venlafaxine, 2009-2014) were conducted. Participants included 640 adults aged 60+ years with DSM-IV-defined GAD (n = 177) or MDD (n = 463). Benzodiazepine data were collected at baseline. Adherence and treatment response were assessed over 12 weeks. The analysis addressed whether coprescribed benzodiazepines are associated with treatment response, antidepressant medication adherence, dropout, final dose of antidepressant medication, and report of antidepressant-related adverse effects. RESULTS: Participants with GAD and coprescribed benzodiazepines were treated with a lower mean dosage of escitalopram and were less likely to complete the trial; there was no difference in adherence or treatment response. Participants with MDD and coprescribed benzodiazepines were less likely to tolerate a therapeutic dose of venlafaxine and reported more medication-related adverse effects; there was no difference in adherence, dropout, or treatment response. CONCLUSIONS: Coprescription of benzodiazepines was associated with increased dropout in older patients with GAD and more medication-related adverse effects in older patients with MDD. However, with the systematic clinical attention offered in a clinical trial, they do not impede treatment response. Clinicians should be aware that a coprescribed benzodiazepine may be a marker of a more challenging treatment course. Trial Registration: Data analyzed were from studies with ClinicalTrials.gov identifiers NCT00892047 and NCT00105586.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".