Observational studyDepression and anxiety2024
Predictive Models for Suicide Attempts in Major Depressive Disorder and the Contribution of
Observational study in Depression and anxiety, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- The Perioperative Neurocognitive Disorder Prediction Based on AI-Assisted EEG Dynamic Features in Anesthetized Mice.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence-Empowered Multimodal Learning in Psychiatry: A Scoping Review.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Review
- Exploring risk signals of association between drugs and suicide: a retrospective investigation from 2004 to 2024.Frontiers in psychiatry · 2026Article
- Clinical, radiological, and radiomics feature-based explainable machine learning models for prediction of neurological deterioration and 90-day outcomes in mild intracerebral hemorrhage.BMC medical imaging · 2025Article
- A Naturalistic Prospective Study of the Prognostic Impact of EPHX2 in Major Depressive Disorder: Impulsivity may be an Important Factor.Depression and anxiety · 2025Observational
- Machine Learning Models to Identify Clinically Significant Anxiety in Short-Term Insomnia Using Accelerometers.Depression and anxiety · 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Suicide is a major public health problem caused by a complex interaction of various factors. Major depressive disorder (MDD) is the most prevalent psychiatric disorder associated with suicide; therefore, it is essential to prioritize suicide prediction and prevention within this population. Integrated information from different dimensions, including personality, cognitive function, and social and genetic factors, is necessary to improve the performance of predictive models. Besides, recent studies have indicated the critical roles for EPHX2/P2X2 in the pathophysiology of MDD. Our previous studies found an association of
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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.