Bibliographic record
Abstract
Generalized anxiety disorder (GAD) is a common, chronic and disabling anxiety disorder with considerable comorbidity with depression as well as with other anxiety disorders. Although tricyclic antidepressants and benzodiazepines have been found to be efficacious in patients with GAD, tolerability problems and other risks limit their use in clinical practice. In placebo-controlled, acute (<8 weeks) trials, several medications, including the selective serotonin reuptake inhibitors ([SSRIs] escitalopram, paroxetine, and sertraline) and others (venlafaxine, buspirone, pregabalin), have demonstrated efficacy in patients with GAD. Indeed, current guidelines for the treatment of GAD recommend SSRIs as first-line pharmacological therapy because of their efficacy and tolerability profiles. Although GAD is a chronic condition that is usually present for years, with symptoms typically fluctuating in intensity over time, there have been few randomized, controlled trials of pharmacotherapy beyond the acute phase of treatment. However, data from recent relapse-prevention studies and longer-term maintenance studies with paroxetine, venlafaxine and escitalopram strongly support the value of continued treatment for at least a further 6 months. This article focuses on pharmacological treatment, and reviews recently available data from acute, long-term and relapse-prevention trials in patients with GAD. In addition, issues relating to the natural course of GAD are highlighted as important considerations to guide selection of pharmacotherapy.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".