The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
LLM ensemble PREVIA predicts future PTSD risk from emergency department trauma narratives with expert-level accuracy
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data
Xu, X., Yao, B., Dong, Y., Gabriel, S., Yu, H., Hendler, J., Ghassemi, M., Dey, A. K., & Wang, D. (2024) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3643540 · Cited by 278
Schultebraucks, K., Yadav, V., Shalev, A. Y., Bonanno, G. A., & Galatzer-Levy, I. R. (2022) · Psychological Medicine · doi:10.1017/s0033291720002718 · Cited by 108
Self-citationBooker, J. A., Graci, M. E., Hudak, L. A., Jovanovic, T., Rothbaum, B. O., Ressler, K. J., Fivush, R., & Stevens, J. (2018) · Journal of Traumatic Stress · doi:10.1002/jts.22271 · Cited by 13
Research works printed but not linked: 64
- Cairns, C. & Kang, K. National Hospital Ambulatory Medical Care Survey: 2022 Emergency Department Summary Tables https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NHAMCS/doc22-ed-508.pdf (2022). · DOI · Paper page
- Diamond, P. R. et al. Change in prevalence of post-traumatic stress disorder in the two years following trauma: a meta-analytic study. Eur. J. Psychotraumatol. 13, 2066456. https://doi.org/10.1080/20008198.2022.2066456 (2022). · DOI
- Roberts, N. P., Kitchiner, N. J., Kenardy, J., Lewis, C. E. & Bisson, J. I. Early psychological intervention following recent trauma: a systematic review and meta-analysis. Eur. J. Psychotraumatol. 10, 1695486. https://doi.org/10.1080/20008198.2019.1695486 (2019). · DOI · Paper page
- Shalev, A. & Barbano, A. PTSD: risk assessment and early management. Psychiatr. Ann. 49, 299–306, https://doi.org/10.3928/00485713-20190605-01 (2019). · DOI
- Marmar, C. R. et al. in Cross-Cultural Assessment of Psychological Trauma and PTSD (eds. John P. Wilson & Catherine So-kum Tang) 197–217 (Springer US, 2007).
- Schultebraucks, K. et al. Development and validation of a brief screener for posttraumatic stress disorder risk in emergency medical settings. Gen. Hosp. Psychiatry 81, 46–50, https://doi.org/10.1016/j.genhosppsych.2023.01.012 (2023). · DOI
- Jaramillo, S., Suffoletto, B., Callaway, C. & Pacella-Labarbara, M. Early screening for posttraumatic stress disorder and depression among injured emergency department patients: a feasibility study. Acad. Emerg. Med. 26, 1232–1244, https://doi.org/10.1111/acem.13816 (2019). · DOI
- Bulger, E. M. et al. Nationwide survey of trauma center screening and intervention practices for posttraumatic stress disorder, firearm violence, mental health, and substance use disorders. J. Am. Coll. Surg. 234, 274–287, https://doi.org/10.1097/xcs.0000000000000064 (2022). · DOI
- Reznek, M. A. et al. Accuracy of physician self-estimation of time spent during patient care in the emergency department. JACEP Open 4, e12923. https://doi.org/10.1002/emp2.12923 (2023). · doi:10.1002/emp2.12923
- Hoot, N. R. & Aronsky, D. Systematic review of emergency department crowding: causes, effects, and solutions. Ann. Emerg. Med. 52, 126–136.e121, https://doi.org/10.1016/j.annemergmed.2008.03.014 (2008). · DOI · Paper page
- Van de Glind, G. et al. Interventions for reducing mental health-related stigma in emergency medicine: an integrative review. J. Emerg. Nurs. https://doi.org/10.1016/j.jen.2025.02.013 (2025). · DOI
- Smith, J. M. et al. An exploration of mental health-related stigma in an emergency setting. Int. J. Ment. Health Addict. 23, 2357–2379, https://doi.org/10.1007/s11469-023-01233-5 (2025). · DOI
- Danner, M. et al. Advancing mental health diagnostics: GPT-based method for depression detection. In: Proc. 62nd Annual Conference of the Society of Instrument and Control Engineers (SICE), 1290-1296. https://doi.org/10.23919/SICE59929.2023.10354236(2023). · DOI
- Lho, S. K. et al. Large language models and text embeddings for detecting depression and suicide in patient narratives. JAMA Netw. Open 8, e2511922, https://doi.org/10.1001/jamanetworkopen.2025.11922 (2025). · DOI
- Ohse, J. et al. Zero-shot strike: testing the generalisation capabilities of out-of-the-box LLM models for depression detection. Comput. Speech Lang. 88, 101663. https://doi.org/10.1016/j.csl.2024.101663 (2024). · DOI
- Wu, Y., Chen, J., Mao, K. & Zhang, Y. Automatic post-traumatic stress disorder diagnosis via clinical transcripts: a novel text augmentation with large language models. In: Proc. IEEE Biomedical Circuits and Systems Conference (BioCAS), 1–5. https://doi.org/10.1109/BioCAS58349.2023.10388714(2023). · DOI · Paper page
- Gozzi, M. & Di Maio, F. Comparative analysis of prompt strategies for large language models: single-task vs. multitask prompts. Electronics 13, 4712, https://doi.org/10.3390/electronics13234712 (2024). · DOI
- Lappin, S. Assessing the strengths and weaknesses of large language models. J. Log., Lang. Inf. 33, 9–20, https://doi.org/10.1007/s10849-023-09409-x (2024). · DOI
- Harrer, S. Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine. EBioMedicine 90, 104512. https://doi.org/10.1016/j.ebiom.2023.104512 (2023). · DOI · Paper page
- Fang, C. et al. LLM-Ensemble: optimal large language model ensemble method for e-commerce product attribute value extraction. Proc. 47th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr. 24, 2910–2914, https://doi.org/10.1145/3626772.3661357 (2024). · DOI
- McLaughlin, K. A. et al. Subthreshold posttraumatic stress disorder in the World Health Organization World Mental Health Surveys. Biol. Psychiatry 77, 375–384, https://doi.org/10.1016/j.biopsych.2014.03.028 (2015). · DOI · Paper page
- Klein, A. B. et al. An empirical investigation of definitions of subthreshold posttraumatic stress disorder. J. Trauma. Stress 37, 113–125, https://doi.org/10.1002/jts.22987 (2024). · DOI
- Brewin, C. R., Andrews, B. & Valentine, J. D. Meta-analysis of risk factors for posttraumatic stress disorder in trauma-exposed adults. J. Consult. Clin. Psychol. 68, 748–766, https://doi.org/10.1037/0022-006x.68.5.748 (2000). · DOI · Paper page
- Gallagher, M. W., Long, L. J. & Phillips, C. A. Hope, optimism, self-efficacy, and posttraumatic stress disorder: a meta-analytic review of the protective effects of positive expectancies. J. Clin. Psychol. 76, 329–355, https://doi.org/10.1002/jclp.22882 (2020). · DOI · Paper page
- Yehuda, R., McFarlane, A. C. & Shalev, A. Y. Predicting the development of posttraumatic stress disorder from the acute response to a traumatic event. Biol. Psychiatry 44, 1305–1313, https://doi.org/10.1016/s0006-3223(98)00276-5 (1998). · DOI · Paper page
- Bryant, R. A. Acute stress disorder as a predictor of posttraumatic stress disorder: a systematic review. J. Clin. Psychiatry 72, 233–239, https://doi.org/10.4088/JCP.09r05072blu (2011). · DOI · Paper page
- Llama Team AI @ Meta. The Llama 3 Herd of Models. https://doi.org/10.48550/arxiv.2407.21783 (2024). · DOI · Paper page
- Cowley, L. E., Farewell, D. M., Maguire, S. & Kemp, A. M. Methodological standards for the development and evaluation of clinical prediction rules: a review of the literature. Diagn. Progn. Res. 3, 16. https://doi.org/10.1186/s41512-019-0060-y (2019). · DOI · Paper page
- Steyvers, M. et al. What large language models know and what people think they know. Nat. Mach. Intell. 7, 221–231, https://doi.org/10.1038/s42256-024-00976-7 (2025). · DOI · Paper page
- Shalev, A. Y. et al. Estimating the risk of PTSD in recent trauma survivors: results of the International Consortium to Predict PTSD (ICPP). World Psychiatry 18, 77–87, https://doi.org/10.1002/wps.20608 (2019). · DOI · Paper page
- Howlett, J. R. & Stein, M. B. Prevention of trauma and stressor-related disorders: a review. Neuropsychopharmacology 41, 357–369, https://doi.org/10.1038/npp.2015.261 (2016). · DOI · Paper page
- Vermetten, E. & McFarlane, A. C. Predicting future risk of PTSD. Nat. Med. 26, 1012–1013, https://doi.org/10.1038/s41591-020-0981-6 (2020). · DOI
- Atilola, O. et al. External locus-of-control partially mediates the association between cumulative trauma exposure and posttraumatic stress symptoms among adolescents from diverse background. Anxiety Stress Coping 34, 626–644, https://doi.org/10.1080/10615806.2021.1891224 (2021). · DOI
- Thompson-Hollands, J., Jun, J. J. & Sloan, D. M. The association between peritraumatic dissociation and PTSD symptoms: the mediating role of negative beliefs about the self. J. Trauma. Stress 30, 190–194, https://doi.org/10.1002/jts.22179 (2017). · DOI
- Vieselmeyer, J., Holguin, J. & Mezulis, A. The role of resilience and gratitude in posttraumatic stress and growth following a campus shooting. Psychol. Trauma 9, 62–69, https://doi.org/10.1037/tra0000149 (2017). · DOI
- Amir, N., Stafford, J., Freshman, M. S. & Foa, E. B. Relationship between trauma narratives and trauma pathology. J. Trauma. Stress 11, 385–392, https://doi.org/10.1023/A:1024415523495 (1998). · DOI · Paper page
- Shalev, A., Liberzon, I. & Marmar, C. Post-traumatic stress disorder. N. Engl. J. Med. 376, 2459–2469, https://doi.org/10.1056/NEJMra1612499 (2017). · DOI · Paper page
- Obradovich, N. et al. Opportunities and risks of large language models in psychiatry. NPP—Digit. Psychiatry Neurosci. 2, 8. https://doi.org/10.1038/s44277-024-00010-z (2024). · DOI
- Yang, K. et al. MentaLLaMA: interpretable mental health analysis on social media with large language models. Proc. ACM Web Conference 4489–4500 (Association for Computing Machinery, 2024). https://doi.org/10.1145/3589334.3648137 · DOI · Paper page
- Guo, Z. et al. Large language models for mental health applications: systematic review. JMIR Ment. Health 11, e57400, https://doi.org/10.2196/57400 (2024). · DOI · Paper page
- Malgaroli, M. et al. Large language models for the mental health community: framework for translating code to care. Lancet Digit. Health 7, e282–e285, https://doi.org/10.1016/s2589-7500(24)00255-3 (2025). · DOI
- Malgaroli, M. & McDuff, D. An overview of diagnostics and therapeutics using large language models. J. Trauma. Stress 37, 754–760, https://doi.org/10.1002/jts.23082 (2024). · DOI
- Fein, J. A., Kassam-Adams, N., Vu, T. & Datner, E. M. Emergency department evaluation of acute stress disorder symptoms in violently injured youths. Ann. Emerg. Med. 38, 391–396, https://doi.org/10.1067/mem.2001.118225 (2001). · DOI
- Schultebraucks, K. et al. Forecasting individual risk for long-term Posttraumatic Stress Disorder in emergency medical settings using biomedical data: a machine learning multicenter cohort study. Neurobiol. Stress 14, 100297. https://doi.org/10.1016/j.ynstr.2021.100297 (2021). · DOI
- Galatzer-Levy, I. R. et al. Early PTSD symptom trajectories: persistence, recovery, and response to treatment: results from the Jerusalem Trauma Outreach and Prevention Study (J-TOPS). PLoS ONE 8, e70084, https://doi.org/10.1371/journal.pone.0070084 (2013). · DOI · Paper page
- Love, J. & Zatzick, D. Screening and intervention for comorbid substance disorders, PTSD, depression, and suicide: a trauma center survey. Psychiatr. Serv. 65, 918–923, https://doi.org/10.1176/appi.ps.201300399 (2014). · DOI · Paper page
- Prater, L. et al. Emergency department and inpatient utilization reductions and cost savings associated with trauma center mental health intervention: results from a 5-year longitudinal randomized clinical trial analysis. Ann. Surg. 279, 17–23, https://doi.org/10.1097/sla.0000000000006102 (2024). · DOI
- Schultebraucks, K. & Chang, B. P. The opportunities and challenges of machine learning in the acute care setting for precision prevention of posttraumatic stress sequelae. Exp. Neurol. 336, 113526. https://doi.org/10.1016/j.expneurol.2020.113526 (2021). · DOI
- Galatzer-Levy, I. R., Huang, S. H. & Bonanno, G. A. Trajectories of resilience and dysfunction following potential trauma: a review and statistical evaluation. Clin. Psychol. Rev. 63, 41–55, https://doi.org/10.1016/j.cpr.2018.05.008 (2018). · DOI · Paper page
- Baranyi, A. et al. Relationship between posttraumatic stress disorder, quality of life, social support, and affective and dissociative status in severely injured accident victims 12 months after trauma. Psychosomatics 51, 237–247, https://doi.org/10.1016/S0033-3182(10)70691-5 (2010). · doi:10.1016/s0033-3182(10)70691-5
- Nicks, B. A. et al. Minimizing attrition for multisite emergency care research. Acad. Emerg. Med. 24, 458–466, https://doi.org/10.1111/acem.13135 (2017). · doi:10.1111/acem.13135
- Schultebraucks, K. et al. A validated predictive algorithm of post-traumatic stress course following emergency department admission after a traumatic stressor. Nat. Med. 26, 1084–1088, https://doi.org/10.1038/s41591-020-0951-z (2020). · DOI
- Qi, W. et al. Application of data pooling to longitudinal studies of early post-traumatic stress disorder (PTSD): the International Consortium to Predict PTSD (ICPP) project. Eur. J. Psychotraumatol. 9, 1476442. https://doi.org/10.1080/20008198.2018.1476442 (2018). · DOI
- Pettit, N. R. et al. Concordance between electronic health record-recorded race and ethnicity and patient report in emergency department patients. Ann. Emerg. Med. 84, 111–117, https://doi.org/10.1016/j.annemergmed.2024.03.025 (2024). · DOI
- Gallegos, I. O. et al. Bias and fairness in large language models: a survey. Comput. Linguist. 50, 1097–1179, https://doi.org/10.1162/coli_a_00524 (2024). · DOI · Paper page
- Jiang, L. Y. et al. Health system-scale language models are all-purpose prediction engines. Nature 619, 357–362, https://doi.org/10.1038/s41586-023-06160-y (2023). · DOI · Paper page
- Yu, Y. et al. Accelerating AI innovation in healthcare: real-world clinical research applications on the Mayo Clinic Platform. npj Health Systems 3, 17. https://doi.org/10.1038/s44401-026-00068-1 (2026). · DOI
- Weathers, F. W. et al. The Clinician-Administered PTSD Scale for DSM–5 (CAPS-5): development and initial psychometric evaluation in military veterans. Psychol. Assess. 30, 383–395, https://doi.org/10.1037/pas0000486 (2018). · DOI · Paper page
- Blevins, C. A., Weathers, F. W., Davis, M. T., Witte, T. K. & Domino, J. L. The Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5): development and initial psychometric evaluation. J. Trauma. Stress 28, 489–498, https://doi.org/10.1002/jts.22059 (2015). · DOI · Paper page
- Beck, A. T. An inventory for measuring depression. Arch. Gen. Psychiatry 4, 561. https://doi.org/10.1001/archpsyc.1961.01710120031004 (1961). · DOI · Paper page
- Beck, A. T., Steer, R. A. & Carbin, M. G. Psychometric properties of the Beck Depression Inventory: twenty-five years of evaluation. Clin. Psychol. Rev. 8, 77–100, https://doi.org/10.1016/0272-7358(88)90050-5 (1988). · DOI · Paper page
- Beck, A. T., Epstein, N., Steer, R. A. & Brown, G. K. An inventory for measuring clinical anxiety: psychometric properties. J. Consult. Clin. Psychol. 56, 893–897 (1988). · DOI · Paper page
- Tortella-Feliu, M. et al. Risk factors for posttraumatic stress disorder: an umbrella review of systematic reviews and meta-analyses. Neurosci. Biobehav. Rev. 107, 154–165, https://doi.org/10.1016/j.neubiorev.2019.09.013 (2019). · DOI · Paper page
- Kuhn, M. & Johnson, K. Applied predictive modeling. https://doi.org/10.1007/978-1-4614-6849-3 (2013). · DOI · Paper page
Metadata
- Authors
- Mueller, V., Yadav, J., Farnsworth, A., Hilberdink, C. E., Gershov, S., Oermann, E. K., Wall, S. P., Shalev, A. Y., & Schultebraucks, K.
- Venue
- Nature Communications
- Published
- 2026-11-09
- Volume
- 17
- Issue
- 1
- ISSN
- 2041-1723
- Cited by
- 0
- References
- 3
- Language
- en
- Licence
- creativecommons.org/licenses/by-nc-nd/4.0 (tdm)
- creativecommons.org/licenses/by-nc-nd/4.0 (vor)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (R01 MH129856)
- U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (R01 HL156134)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: LLM ensemble PREVIA predicts future PTSD risk from emergency department trauma narratives with expert-level accuracy. https://app.cochat.ai/papers/10.1038/s41467-026-78768-3
- The paper
- Mueller, V., Yadav, J., Farnsworth, A., Hilberdink, C. E., Gershov, S., Oermann, E. K., Wall, S. P., Shalev, A. Y., & Schultebraucks, K. (2026). LLM ensemble PREVIA predicts future PTSD risk from emergency department trauma narratives with expert-level accuracy. Nature Communications. https://doi.org/10.1038/s41467-026-78768-3
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.