ArticleNpj mental health research2025
Prediction of individual patient outcomes to psychotherapy vs medication for major depression.
Article in Npj mental health research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning in the prediction of treatment response for emotional disorders: A systematic review and meta-analysis.Clinical psychology review · 2025Pooled it
- Development of a baseline battery to optimize depression treatment assignment in primary care: Balancing breadth and brevity.Journal of affective disorders · 2026Article
- Sex Differences in Depression: Adult Cytogenesis as Potential Target for Precision Psychiatry.Cells · 2026Review
- Blocking the Reflection: Milestones and Hurdles for Digital Twins in Mental Health.Pharmacopsychiatry · 2026Review
- Prediction of 12-Week Remission in Patients With Depressive Disorder Using Reasoning-Based Large Language Models: Model Development and Validation Study.JMIR mental health · 2026Article
- A critical appraisal of integrating family constellation therapy with pharmacological treatment for depression.Frontiers in psychiatry · 2026Review
- Anxiety and depression in emerging adults: The STAND program as a model of scalable screening and intervention.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- Integration of Multi-Modal Biosensing Approaches for Depression: Current Status, Challenges, and Future Perspectives.Sensors (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Treatments for major depressive disorder (MDD) include antidepressant medications and evidence-based psychotherapies, which are approximately equally efficacious. Individual response to treatment, however, is variable, implying individual differences that could allow for prospective differential prediction of treatment response and personalized treatment recommendation. We used machine learning to develop predictor variables that combined demographic and clinical items from a randomized clinical trial. The variables predicted a meaningful proportion of variance in end-of-treatment depression severity for cognitive behavioral therapy (39.7%), escitalopram (32.1%), and duloxetine (67.7%), leading to a high accuracy in predicting remission (71%). Further, we used these variables to simulate treatment recommendation and found that patients who received their recommended treatment had significantly improved depression severity and remission likelihood. Finally, the prediction algorithms and treatment recommendation tool were externally validated in an independent sample. These results represent a highly promising, easily implemented, potential advance for personalized medicine in MDD treatment.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.