The Efficacy of Measurement-Based Care for Depressive Disorders
Bibliographic record
Abstract
To determine the efficacy of measurement-based care (MBC), defined as routinely administered outcome measures with practitioner and patient review to inform clinical decision-making, for adults with depressive disorders. Embase, MEDLINE, PsycINFO, ClinicalTrials.gov, CNKI, and Wanfang Data were searched through July 1, 2020, using search terms for measurement-based care, depression, antidepressant or pharmacotherapy, and randomized controlled trials (RCTs), without language restriction. Of 8,879 articles retrieved, 7 RCTs (2,019 participants) evaluating MBC for depressive disorders, all involving pharmacotherapy, were included. Two independent reviewers extracted data. The primary outcome was response rate (≥ 50% improvement from baseline to endpoint on a depression scale). Secondary clinical outcomes were remission rate (endpoint score in remission range), difference in endpoint severity, and medication adherence. = .001). Although benefits for clinical response are unclear, MBC is effective in decreasing depression severity, promoting remission, and improving medication adherence in patients with depressive disorders treated with pharmacotherapy. The results are limited by the small number of included trials, high risk of bias, and significant study heterogeneity.
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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 | 0.020 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".