A Qualitative Review of Recent Economic Evaluations of Escitalopram
Bibliographic record
Abstract
Objective: To review published pharmacoeconomic evaluations of escitalopram. Data Sources: MEDLINE, EMBASE, Health Star, and Ovid Journals databases were searched using escitalopram, cost, cost-effectiveness, and economics as search terms. All articles and abstracts published from January 2003 to April 2006 were reviewed and cross-referenced for possible exclusions or duplications. Searches were not limited to English-language publications. Study Selection and Data Extraction: One prospective economic study and 10 studies using decision analytical models assessing the cost-effectiveness of escitalopram compared with citalopram and/or venlafaxine were identified and reviewed. Data Synthesis: Pharmacoeconomic studies using country-specific currency economic analysis from Europe and Canada have been conducted assessing the cost-effectiveness of escitalopram in major depression. Several studies have shown escitalopram to be more cost-effective compared with citalopram, with cost savings identified in societal and healthcare system perspectives. However, the cost-effectiveness of escitalopram was less significant when compared with venlafaxine. Conclusions: Economic studies suggest that escitalopram is cost-effective compared with citalopram in treatment of major depression, but has marginal advantage compared with venlafaxine.
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 | 0.049 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".