The Pharmacologic Treatment of Anxiety Disorders
Bibliographic record
Abstract
Anxiety disorders, as a group, are among the most common mental health conditions and frequently cause significant functional impairment. Both psychotherapeutic and pharmacologic techniques are recognized to be effective management strategies. This review provides a discussion of the major classes of psychotropic medications investigated in clinical trials of the following anxiety disorders: panic disorder, social anxiety disorder, generalized anxiety disorder, posttraumatic stress disorder, and obsessive-compulsive disorder. Findings suggest that both selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors are useful first-line agents for most of the anxiety disorders, particularly given the frequent comorbidity with mood disorders. Highly serotonergic agents are preferred for obsessive-compulsive disorder. Other antidepressants, such as tricyclic antidepressants or monoamine oxidase inhibitors, are generally reserved as second- and third-line strategies due to tolerability issues. Evidence for other agents, including anticonvulsants and atypical antipsychotics, suggests that they may have an adjunctive role to antidepressants in cases of treatment resistance, while azapirones have been used effectively for generalized anxiety disorder, and a substantial body of evidence supports benzodiazepine use in panic disorder and generalized anxiety disorder. Despite notable advances, many patients with anxiety disorders fail to adequately respond to existing pharmacologic treatments. Increased research attention should be focused on systematizing pharmacologic and combined pharmacologic-psychosocial strategies to address treatment resistance and developing novel treatments for anxiety disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 0.001 |
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".