Comparison of the Efficacy Between Standard Measurement-Base Care (MBC) and Enhanced MBC for Major Depressive Disorder: A Pilot Study
Bibliographic record
Abstract
Purpose: To validate the efficacy of enhanced measurement-based care against standard measurement-based care in patients with major depressive disorder. Patients and Methods: In this pilot study of an ongoing multicenter cluster randomized controlled trails, 160 patients diagnosed with major depressive disorder were enrolled from 2 mental health centers, with a plan to include 12 centers in total. One hundred patients engaged in a six-month evaluation using a technology-enhanced measurement-based care tool, including assessments of clinical symptoms, side effects, and functionality at baseline, two months, four months and six months. Simultaneously, the remaining 60 patients underwent standard paper-based measurement-based care, utilizing the same set of scales over the same six-month period, with assessments at the same time points. Results: Patients utilizing the enhanced measurement-based care tool demonstrated a significantly higher reduction rate in PHQ-9 scores compared to those using standard paper-based measurement-based care during the two-month follow-up. Additionally, a notable positive correlation was observed between the frequency of enhanced measurement-based care tool usage and the quality of life during the two-month follow-up. Conclusion: Enhanced measurement-based care has the effect of reducing depressive symptoms. Our study emphasized that using enhanced measurement-based care via smartphones is a feasible tool for patients with major depressive disorder. Our future study, including results from additional research centers, may further validate the effectiveness of enhanced measurement-based care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".