Economic evaluations of treatment of depressive disorders in adolescents: Protocol for a scoping review
Bibliographic record
Abstract
AIM: Depressive disorders in adolescents are common and impairing. Evidence-based treatments are available; however, at a cost. In the context of the COVID-19 pandemic, we anticipate increased demand for treatment services for adolescents with depression. We also anticipate that economic resources will be strained. Identifying cost-effective strategies to optimally treat depression in adolescents is imperative. This protocol for a scoping review aims to describe the literature with respect to economic evaluations of treatments for depression in adolescents. METHODS: We will conduct a scoping review using established methods and reporting guidelines. MEDLINE, Embase, PsyclNFO, Econlit, and the International HTA Database will be searched from inception to June 13, 2023, with an update closer to time of manuscript submission, while the NHS Economic Evaluation Database archives will be searched from inception to December 2014. Publications that contain economic evaluations, in the context of a clinical trial or a model-based study, testing a treatment of depression in adolescents will be selected for inclusion. Extracted data items will include: economic evaluation perspectives, health outcome variables and costs used in economic evaluations, types of analyses performed, as well as quality of reporting and methodology. RESULT: A narrative synthesis with summary tables will be used to describe our findings. CONCLUSION: Our findings will help identify gaps in the literature with respect to economic analyses for the treatment of depression such that these gaps can be filled with future research. Policy-makers, funders and administrators may also use our findings to inform their decisions around provision of various treatments for depression in adolescents. REGISTRATION: osf.io/5fteb (note that information on this link will be updated upon acceptance for publication based on reviewer comments).
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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 | 0.100 | 0.137 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.017 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.112 | 0.018 |
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".