Efficacy, effectiveness and efficiency of escitalopram in the treatment of major depressive and anxiety disorders
Bibliographic record
Abstract
In addition to the large personal challenge that depression and anxiety present, these disorders are associated with a substantial burden of disability and lost productivity, and are responsible for considerable strain on healthcare resources and on society. Escitalopram is recommended as first-line therapy for the treatment of major depressive disorder and severe depression, and is indicated in anxiety disorders. Compared with other antidepressants, escitalopram has equal or superior efficacy, as proven in clinical trial settings, equal or superior real-life effectiveness, established in both clinical and observational studies, and a better tolerability profile. While drug acquisition costs are higher for escitalopram than for generic drugs such as fluoxetine and citalopram, numerous prospective and modeled economic analyses show that associated direct and indirect costs of treatment are lower with escitalopram than with citalopram, fluoxetine, sertraline and venlafaxine. Thus, escitalopram appears to be more economically efficient than many antidepressants currently available. Escitalopram has a prominent role in the treatment of major depressive disorder and anxiety disorders, and may also prove to be important in the treatment of mixed depressive anxiety disorder.
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 | 0.003 | 0.005 |
| 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 | 0.000 |
| 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".