SynthesisPloS one2017
Models to predict relapse in psychosis: A systematic review.
Synthesis in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic models for predicting overall survival in metastatic castration-resistant prostate cancer: a systematic review.World journal of urology · 2020Pooled it
- Digital smartphone intervention to recognise and manage early warning signs in schizophrenia to prevent relapse: the EMPOWER feasibility cluster RCT.Health technology assessment (Winchester, England) · 2022Trial
- Perturbations of whole-brain model reveal critical areas related to relapse of early psychosis.Network neuroscience (Cambridge, Mass.) · 2026Article
- Patient Perspectives on AI-Driven Predictions of Schizophrenia Relapses: Understanding Concerns and Opportunities for Self-Care and Treatment.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2024Article
- Psychosis Relapse Prediction Leveraging Electronic Health Records Data and Natural Language Processing Enrichment Methods.Frontiers in psychiatry · 2022Article
- Individualized prediction of three- and six-year outcomes of psychosis in a longitudinal multicenter study: a machine learning approach.NPJ schizophrenia · 2021Article
- Identification of Risk Factors to Predict the Occurrences of Relapses in Individuals with Schizophrenia Spectrum Disorder in Iran.International journal of environmental research and public health · 2021Article
- Development and external validation of an admission risk prediction model after treatment from early intervention in psychosis services.Translational psychiatry · 2021Article
- Are we getting any better at staying better? The long view on relapse and recovery in first episode nonaffective psychosis and schizophrenia.Therapeutic advances in psychopharmacology · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere is little evidence on the accuracy of psychosis relapse prediction models. Our objective was to undertake a systematic review of relapse prediction models in psychosis.
methodWe conducted a literature search including studies that developed and/or validated psychosis relapse prediction models, with or without external model validation. Models had to target people with psychosis and predict relapse. The key databases searched were; Embase, Medline, Medline In-Process Citations & Daily Update, PsychINFO, BIOSIS Citation Index, CINAHL, and Science Citation Index, from inception to September 2016. Prediction modelling studies were assessed for risk of bias and applicability using the PROBAST tool.
resultsThere were two eligible studies, which included 33,088 participants. One developed a model using prodromal symptoms and illness-related variables, which explained 14% of relapse variance but was at high risk of bias. The second developed a model using administrative data which was moderately discriminative (C = 0.631) and associated with relapse (OR 1.11 95% CI 1.10, 1.12) and achieved moderately discriminative capacity when validated (C = 0.630). The risk of bias was low.
conclusionsDue to a lack of high quality evidence it is not possible to make any specific recommendations about the predictors that should be included in a prognostic model for relapse. For instance, it is unclear whether prodromal symptoms are useful for predicting relapse. The use of routine data to develop prediction models may be a more promising approach, although we could not empirically compare the two included studies.
Indexed as
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.