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Measurement-Based Care to Enhance Antidepressant Treatment Outcomes in Major Depressive Disorder

2025· article· en· W4413914719 on OpenAlexaff
Muhammad Ishrat Husain, Zahra Nigah, Sami Ansari, Ameer B. Khoso, Tayyeba Kiran, Madeha Umer, Moin Ahmed Ansari, Moti Ram Bhatia, Sylvia Khan, Muhammad Omair Husain, Abdul Malik, Haider Naqvi, Altaf Qadir, Aatir H. Rajput, Muhammad Saqib, Muhammad Ilyas, Mujeeb Ullah Khan Doutani, Silsila Sherzad, Khaizran Siddiqui, Zona Tahir, Wei Wang, Nusrat Husain, Nasim Chaudhry, Imran B. Chaudhry, Benoit H. Mulsant

Bibliographic record

VenueJAMA Network Open · 2025
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsThe Scarborough HospitalUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsMajor depressive disorderMedicineParoxetineRating scaleDepression (economics)Psychological interventionMirtazapineRandomized controlled trialHamilton Rating Scale for DepressionPsychiatryAntidepressantAdverse effectClinical trialPhysical therapyInternal medicinePsychologyMoodAnxiety

Abstract

fetched live from OpenAlex

Importance: Measurement-based care (MBC) guides clinical decisions through structured monitoring of symptoms and adverse effects. Although MBC has been associated with improved outcomes in major depressive disorder (MDD), its effectiveness in low- and middle-income countries (LMICs) remains understudied. Objective: To assess whether MBC accelerates the resolution of depressive symptoms compared with standard care among adults with MDD in Pakistan. Design, Setting, and Participants: This multicenter, assessor-blinded, parallel-arm randomized clinical trial was conducted in Pakistan from September 2022 to January 2024, with 24 weeks of follow-up. Adults diagnosed with nonpsychotic MDD were recruited from psychiatric hospitals and primary care centers in 7 Pakistani cities (Karachi, Lahore, Rawalpindi, Hyderabad, Peshawar, Multan, and Quetta). Participants were randomized 1:1 to MBC or standard care. Intention-to-treat analyses were conducted. Interventions: By design, pharmacotherapy was limited to paroxetine or mirtazapine in both MBC or standard care groups. The MBC group completed the 16-item Quick Inventory of Depressive Symptomatology-Self-Report and the Frequency, Intensity, and Burden of Side Effects Rating Scale at each visit (baseline and weeks 2, 4, 8, 12, and 24). Scores from these instruments informed antidepressant dose adjustments or switch. The standard care group received treatment based on clinician judgment and did not undergo repeated clinical measurements. Main Outcomes and Measures: Primary outcomes were time to response (defined as ≥50% reduction in the 17-item Hamilton Depression Rating Scale [HDRS-17]; range: 0-52, with the highest score indicating severe depression) and time to remission (defined as HDRS-17 score of ≤7) within the 24-week follow-up period. Secondary outcomes included changes in HDRS-17 scores and rates of adverse effects or treatment discontinuation. Results: A total of 154 adults (mean [SD] age, 34.5 [10.5] years; 105 females [68.2%]) were randomized. Median (IQR) time to response was faster with MBC than with standard care (2 [2-4] weeks vs 4 [2-12] weeks); similarly, median (IQR) time to remission was faster for MBC vs standard care (4 [4-8] weeks vs 8 weeks [2 weeks to no remission] weeks). At week 24, there were no significant differences in rates of response or remission between groups. After week 24, reduction in mean HDRS-17 scores was significantly but modestly greater in the MBC group than in the standard care group (-18.1 [95% CI, 16.4-19.6] points vs -17.0 [95% CI, 15.6-18.5] points; t129 = 0.71; P < .001). No differences were observed in other secondary outcomes. Conclusions and Relevance: This trial of adults with MDD found that MBC led to faster time to response and time to remission than standard care in low-resource settings. Future studies need to confirm the clinical effectiveness of MBC and assess its cost-effectiveness in LMICs. Trial Registration: ClinicalTrials.gov Identifier: NCT05431374.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.334
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2025
Admission routes1
Has abstractyes

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