Observational studyMolecular psychiatry2025
Generalizability of clinical prediction models in mental health.
Observational study in Molecular psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence approaches for schizophrenia prediction and its biomarkers using medical imaging data.Frontiers in psychiatry · 2026Pooled it
- Explainable and High-Performance ECG-Informed Machine Learning and Deep Learning Framework for Cardiovascular Risk Prediction.Life (Basel, Switzerland) · 2026Article
- Article
- Distinguishing Chronic and Non-Chronic Depression: A Clinical Profile and Symptom Networks Approach.Psychotherapy and psychosomatics · 2026Article
- Five tenets for advancing evidence-based precision medicine.Nature medicine · 2026Review
- From imaging to omics: deep learning is bridging MRI and liquid biopsy in bone tumor diagnosis.Journal of bone oncology · 2026Review
- A multi-task deep learning and radiomics framework for fetal anatomical structure detection and classification in ultrasound imaging.Scientific reports · 2026Article
- Advanced biomaterials and digital twins for precision psychiatry: neuroimmune modulation and AI-guided therapeutics.Frontiers in bioengineering and biotechnology · 2026Article
- Machine learning strategies for predicting pediatric suicidal behaviors in a Brazilian emergency setting.Frontiers in artificial intelligence · 2026Article
- Development and validation of a machine learning-based risk prediction model for non-suicidal self-injury in adolescents.Frontiers in psychiatry · 2026Article
- The role of cognitive function in predicting metabolic risk in schizophrenia: a multi-model comparison incorporating clinical features.Frontiers in psychiatry · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
24 authors.
Funding
Abstract
Concerns about the generalizability of machine learning models in mental health arise, partly due to sampling effects and data disparities between research cohorts and real-world populations. We aimed to investigate whether a machine learning model trained solely on easily accessible and low-cost clinical data can predict depressive symptom severity in unseen, independent datasets from various research and real-world clinical contexts. This observational multi-cohort study included 3021 participants (62.03% females, M
Indexed as
Identifiers
What Socratic 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 Socratic graph.