ArticleBMC psychiatry2020
Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach.
Article in BMC psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02010619 (Internet Delivered Cognitive Behavior Therapy for Body Dysmorphic Disorder - a Randomized Controlled Trial), which is not on this map. Cited by 24 papers, 3 of them syntheses that pooled it.
What it found
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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.
Internet Delivered Cognitive Behavior Therapy for Body Dysmorphic Disorder - a Randomized Controlled Trial
Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Effects of Pharmacological and Psychological Treatments on Depressive Symptoms in Body Dysmorphic Disorder: A Systematic Review and Meta-Analysis.Current psychiatry reports · 2026Pooled it
- Machine learning in the prediction of treatment response for emotional disorders: A systematic review and meta-analysis.Clinical psychology review · 2025Pooled it
- Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review.Frontiers in psychiatry · 2024Pooled it
- Comparison between clinician and machine learning prediction in a randomized controlled trial for nonsuicidal self-injury.BMC psychiatry · 2024Trial
- Predicting response to a smartphone-based cognitive-behavioral therapy for body dysmorphic disorder.Journal of affective disorders · 2024Trial
- Predictors of pharmacotherapy outcomes for body dysmorphic disorder: a machine learning approach.Psychological medicine · 2023Trial
- Mechanisms of cognitive behavioral therapy vs. supportive psychotherapy in body dysmorphic disorder: An exploratory mediation analysis.Behaviour research and therapy · 2023Trial
- Predictors and moderators of symptom change during cognitive-behavioral therapy or supportive psychotherapy for body dysmorphic disorder.Journal of affective disorders · 2021Trial
- Psycho Dermatological Dimensions of Body Dysmorphic Disorder: A Comprehensive Review of Diagnostic Criteria, Treatment Options, and Clinical Outcomes in Dermatological and Cosmetic Surgery Settings.Aesthetic plastic surgery · 2026Review
- Maternal Accommodation of Adolescent Body Dysmorphic Disorder: Clinical Correlates and Association with Treatment Outcomes.Child psychiatry and human development · 2026Article
- Predicting Therapy Outcomes in Patients With Stress-Related Disorders: Protocol for a Predictive Modeling Study.JMIR research protocols · 2025Article
- Integration of AI into psychodermatology: concern for racial bias.Archives of dermatological research · 2025Article
- Advancing Psychosocial Treatment for Body Dysmorphic Disorder: A State-of-the-Science Review.Behavior therapy · 2024Review
- The future of psychodermatology: integrating artificial intelligence into practice.Archives of dermatological research · 2024Article
- Exploring the Intersection of Body Dysmorphic Disorder (BDD) and Dermatological Conditions: A Narrative Review.Dermatology and therapy · 2024Review
- Artificial intelligence in psychodermatology: A brief report of applications and impact in clinical practice.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Review
- A primer on the use of machine learning to distil knowledge from data in biological psychiatry.Molecular psychiatry · 2024Review
- Who benefits from guided internet-based interventions? A systematic review of predictors and moderators of treatment outcome.Internet interventions · 2023Review
- Credibility and expectancy of smartphone-based cognitive behavioral therapy among adults with body dysmorphic disorder.Journal of obsessive-compulsive and related disorders · 2023Article
- Deep learning for the prediction of clinical outcomes in internet-delivered CBT for depression and anxiety.PloS one · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundPrevious attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models.
methodsThis study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses.
resultsRandom forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68, 66 and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD.
conclusionsThe results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD.
trial registrationClinicalTrials.gov ID: NCT02010619.
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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.