Systematic review of structured care pathways in major depressive disorder and bipolar disorder
Bibliographic record
Abstract
BACKGROUND: Structured care pathways (SCPs) consist of treatment algorithms that patients advance through with the goal of achieving remission or response. These SCPs facilitate the application of current evidence and adequate treatment, which potentially benefit patients with mood disorders. The aim of this systematic review was to provide an updated synthesis of SCPs for the treatment of depressive disorders and bipolar disorder (BD). METHOD: PubMed, PsycINFO, and Embase were searched through June 2022 for peer-reviewed studies examining outcomes of SCPs. Eligibility criteria included being published in a peer-reviewed journal in the English language, reporting of intervention used in the SCP, and having quantitative outcomes. Studies Cochrane risk of bias tool was used to assess quality of RCTs. RESULTS: Thirty-six studies including 15,032 patients were identified for qualitative synthesis. Six studies included patients with BD. The studies were highly heterogeneous in design, outcome measures, and algorithms. More than half of the studies reported superiority of SCPs over treatment as usual, suggesting that the standardized structure and consistent monitoring inherent in SCPs may be contributing to their effectiveness. We also found accumulating evidence supporting feasibility of SCPs in different settings, although dropout rates were generally higher in SCPs. The studies included were limited to being published in peer-reviewed journals in English language. The heterogeneity of studies did not allow quantitative evaluation. CONCLUSIONS: The findings of our study suggest that SCPs are equally or more effective than treatment as usual in depression and BD. Further studies are required to ascertain their effectiveness, particularly for BD, and to identify factors that influence their feasibility and success.
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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 | 0.018 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".