A Multicenter Randomized Controlled Trial of Measurement-Based Care for Major Depressive Disorder in Pakistan
Bibliographic record
Abstract
Aims A worldwide public health concern is major depressive disorder (MDD) with limited availability and access to evidence-based treatment in low- and middle-income countries (LMICs) such as Pakistan. Measurement-based care (MBC) is a low-cost strategy to improve clinical outcomes for people with MDD that involves the systematic administration of validated outcome measures to inform treatment decisions. However, research on MBC's effectiveness in LMICs is scarce. This paper aims to evaluate the feasibility and clinical effectiveness of MBC against standard care for patients with moderate to severe MDD in Pakistan. Methods This is a multicenter randomized control trial. Participants (n = 154) of 18 to 65 years of age recruited from psychiatric units of teaching and non-teaching hospitals and primary care settings such as General Physician (GP) clinics and Basic Health Units (BHUs) from 6 cities were randomised to receive MBC (guided by a schedule), or standard treatment (guided by clinicians’ judgement). Patients were prescribed by treating clinicians either with mirtazapine (7.5–45 mg/day) or paroxetine (10–60 mg/day) for a period of 12 weeks. All participants, regardless of their treatment arm, were followed-up till 24 weeks post-randomization and assessed for severity of depression. Side effects were regularly monitored using standard checklist. Outcome assessors were blind to treatment allocation. Results The Pakistani National Bioethics Committee (NBC) has granted complete ethical approval. A total of 15 psychiatrists and 4 General Practitioners (GPs) were approached and invited to participate in the study and consent was given by 9 psychiatrists and 2 GPs. A total of 351 patients were screened against eligibility criteria and 177 were eligible to participate. A total of 154 eligible participants consented (87%) to participate and were recruited and randomized into the trial. A total of 131 randomized participants (85%) completed 24-month follow-up. Only two adverse events were reported during the trial period. Recruitment, retention and safety analysis indicates feasibility of the trial in Pakistani healthcare context. The data are being analyzed for effectiveness outcomes. Conclusion It is essential to investigate the viability, usefulness, and efficacy of MBC for MDD in low-resource settings due to mounting data from high-income settings confirming its effectiveness. The planned trial's outcomes may help build a scalable, low-cost method for effectively improving outcomes for MDD patients in Pakistan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".