Cost-effectiveness of selective serotonin reuptake inhibitors and routine specialist care with and without cognitive–behavioural therapy in adolescents with major depression
Bibliographic record
Abstract
BACKGROUND: Major depression is an important and costly problem among adolescents, yet evidence to support the provision of cost-effective treatments is lacking. AIMS: To assess the short-term cost-effectiveness of combined selective serotonin reuptake inhibitors (SSRIs) and cognitive-behavioural therapy (CBT) together with clinical care compared with SSRIs and clinical care alone in adolescents with major depression. METHOD: Pragmatic randomised controlled trial in the UK. Outcomes and costs were assessed at baseline, 12 and 28 weeks. RESULTS: The trial comprised 208 adolescents, aged 11-17 years, with major or probable major depression who had not responded to a brief initial psychosocial intervention. There were no significant differences in outcome between the groups with and without CBT. Costs were higher in the group with CBT, although not significantly so (P=0.057). Cost-effectiveness analysis and exploration of the associated uncertainty suggest there is less than a 30% probability that CBT plus SSRIs is more cost-effective than SSRIs alone. CONCLUSIONS: A combination of CBT plus SSRIs is not more cost-effective in the short-term than SSRIs alone for treating adolescents with major depression in receipt of routine specialist clinical care.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".