SynthesisBMC psychiatry2026
Speech-based computational approaches for classification and symptom monitoring in schizophrenia spectrum disorders: a systematic review and meta-analysis.
Synthesis in BMC psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Effects of crocin on anxiety, depression, and hippocampal BDNF expression in streptozotocin-induced diabetic rats.Annals of medicine and surgery (2012) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis systematic review and meta-analysis evaluated speech-based computational approaches for diagnosing schizophrenia and assessing symptom severity.
methodsThis study is a systematic review and meta-analysis conducted in accordance with PRISMA guidelines. A comprehensive search of PubMed, Scopus, and Web of Science databases was performed from inception to August 2025. Eligible studies included individuals diagnosed with schizophrenia and employed computational analyses of speech or language, such as natural language processing, acoustic feature extraction, or large language models, and reported diagnostic accuracy metrics or associations with validated clinical scales. Two independent reviewers screened titles, abstracts, and full texts, extracted data, and assessed methodological quality. When sufficient quantitative data were available, random-effects meta-analysis models were applied to pool diagnostic performance and correlation estimates. Diagnostic performance refers to supervised classification accuracy relative to clinician-assigned reference diagnoses rather than autonomous clinical diagnosis.
resultsThirty-nine studies met inclusion criteria, with 16 contributing data to meta-analysis. Pooled diagnostic performance was high (AUC≈0.84), while speech-derived features showed moderate association with symptom severity (r ≈ 0.34). Large language models and multimodal pipelines outperformed traditional machine-learning and unimodal systems. Speech measures were most sensitive to negative symptoms, including alogia and reduced affect, whereas coherence-based metrics captured thought disorder. Sensitivity analyses confirmed robustness, and no meaningful publication bias was observed.
conclusionComputational speech analysis demonstrates strong potential as an objective, scalable adjunct for schizophrenia assessment. Future research should prioritize standardized protocols, larger multicenter cohorts, and real-world clinical integration to support translation into practice. CLINICAL
trial registrationNot applicable. CLINICAL TRIAL NUMBER: Not applicable.
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.