Cost effectiveness analysis of escitalopram compared to venlafaxine and fluvoxamine in treatment of major depressive disorder
Bibliographic record
Abstract
Objective. To compare the costs and effectiveness of escitalopram with venlafaxine and fluvoxamine for treatment of major depressive disorder (MDD) from the societal perspective in Singapore. Methods. The decision analytical model consisted of two pathways, one for primary care and the other for secondary care over a time horizon of 6 months. The parameters in the model were derived from clinical trials and results of a survey on local general practitioners and psychiatrists. The proportion of patients successfully treated was the main effectiveness measurement. Both direct and indirect costs were estimated and reported in 2007 Singapore dollars. Deterministic and probabilistic sensitivity analyses were performed. Results. The overall success rate for the 6-month treatment was 68.1% for escitalopram compared to 66.0% for venlafaxine. The total costs per patient treated were $2845 for escitalopram compared to $3176 for venlafaxine. The overall success rate was 64.7% for escitalopram and 60.0% for fluvoxamine. The total costs per patient treated were $3133 for escitalopram compared to $3297 for fluvoxamine. Probability sensitivity analysis demonstrated that escitalopram was dominant to venlafaxine and fluvoxamine in more than 95% of the random samples. Conclusion. Escitalopram is a cost-effective pharmacotherapy for MDD compared to venlafaxine and fluvoxamine.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".