Bibliographic record
Abstract
Anxiety disorders are highly prevalent, come in many forms and are often chronic, with many patients requiring long-term maintenance therapy. Anxiety and depression may also be comorbid in up to 50% of patients, leading to problems during diagnosis and treatment. Despite their frequency, the recognition and treatment of anxiety disorders is frequently suboptimal, with as few as 15% of patients obtaining treatment consistent with evidence-based care recommendations. Current treatment guidelines for anxiety disorders include a range of pharmacological and non-pharmacological approaches. However, the use of these guidelines alone may not be sufficient to improve patient outcomes. Optimal treatments for anxiety should be based on chronic disease management and balance efficacy with long-term tolerability. Current first-line therapies should include broad-spectrum agents that have proven efficacy in treating both anxiety and depression and are effective across all treatment phases. The allosteric serotonin reuptake inhibitor (ASRI), escitalopram, is a particularly effective treatment, offering high rates of remission combined with relatively low rates of discontinuation due to adverse events. Combination therapy involving medication and psychological approaches, e.g., cognitive behavioral therapy, may also be helpful. Novel approaches to delivering psychotherapy and self-management via the Internet may address accessibility issues for evidence-based psychological treatments.
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 | 0.072 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.027 | 0.038 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".