ArticleJournal of interpersonal violence2022
Predicting Posttraumatic Stress Disorder Among Survivors of Recent Interpersonal Violence.
Article in Journal of interpersonal violence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses 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
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 26 citations in OpenAlex.
- The Role of Artificial Intelligence for Intimate Partner Violence Prevention: A Systematic Review.Journal of clinical nursing · 2026Pooled it
- Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Predicting longitudinal post-traumatic stress disorder symptoms using psychological measures and heart rate variability: a machine-learning approach.European journal of psychotraumatology · 2026Article
- Unmasking deep-rooted trauma: long-term effects of childhood adversities on posttraumatic stress disorder in healthcare workers facing acute multi-trauma.Frontiers in psychiatry · 2026Article
- Article
- A systematic review of machine learning findings in PTSD and their relationships with theoretical models.Nature. Mental health · 2025Article
- Mental Health and Quality of Life in Ecuadorian Women Exposed to Gender-Based Violence.Journal of interpersonal violence · 2024Article
- Interpersonal violent injury and subsequent risk of suicide and deliberate self-harm: a register-based national cohort study from Norway, 2010-2018.EClinicalMedicine · 2024Article
- Diversity in emotion regulation strategy use: Resilience against posttraumatic stress disorder.Behaviour research and therapy · 2024Article
- Advances in the molecular neurobiology of posttraumatic stress disorder from global contexts: A systematic review of longitudinal studies.Global mental health (Cambridge, England) · 2023Review
- Concentrations of Salivary Cortisol in Victims of Intimate Partner Violence According to the CIRCORT Database.International journal of environmental research and public health · 2021Article
- Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach.PloS one · 2021Article
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 at 5 institutions in 2 countries.
Funding
Abstract
A substantial minority of women who experience interpersonal violence will develop posttraumatic stress disorder (PTSD). One critical challenge for preventing PTSD is predicting whose acute posttraumatic stress symptoms will worsen to a clinically significant degree. This 6-month longitudinal study adopted multilevel modeling and exploratory machine learning (ML) methods to predict PTSD onset in 58 young women, ages 18 to 30, who experienced an incident of physical and/or sexual assault in the three months prior to baseline assessment. Women completed baseline assessments of theory-driven cognitive and neurobiological predictors and interview-based measures of PTSD diagnostic status and symptom severity at 1-, 3-, and 6-month follow-ups. Higher levels of self-blame, generalized anxiety disorder severity, childhood trauma exposure, and impairment across multiple domains were associated with a pattern of high and stable posttraumatic stress symptom severity over time. Predictive performance for PTSD onset was similarly strong for a gradient boosting machine learning model including all predictors and a logistic regression model including only baseline posttraumatic stress symptom severity. The present findings provide directions for future work on PTSD prediction among interpersonal violence survivors that could enhance early risk detection and potentially inform targeted prevention programs.
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.