Evidence map›Paper›PMID 41062532›Full record

ArticleScientific reports2025

Modeling psychopathology in high-dimensional vector space using the high-dimensional symptom space (HDSS) model can operationalize precision psychiatry in US adolescents.

Marcus G Wild, Rebecca A Cutler

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Marcus G WildUnited States Department of Veterans Affairs, VHA VISN 17 Center of Excellence for Research on Returning War Veterans, 4800 Memorial Drive (151C), Waco, TX, 76711, USA. marcus.wild@va.gov.
Rebecca A CutlerUniversity of Texas at Austin, Austin, TX, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Symptoms of psychopathology vary across people, limiting inferences about origins and treatments of disorders for any one person. The high-dimensional symptom space (HDSS) model offers a novel framework for understanding psychopathology by representing symptoms as vectors within a multidimensional space. Unlike traditional categorical and dimensional models, HDSS uses geometric distances to empirically characterize a person's unique experience of symptoms, with the option to integrate social and cultural factors for more precise, personalized treatments. Using data from the adolescent brain and cognitive development (ABCD) study, we demonstrate that HDSS preserves individual specificity, effectively captures dynamic trajectories of psychological distress, and accommodates clinical heterogeneity. Results indicate that HDSS distances correspond to symptom severity and capture nuanced patterns of psychological distress over time, offering a comprehensive and individualized understanding of psychopathology. This model allows for a person-centered understanding of psychopathology, highlighting unique symptom patterns and their evolution over time. HDSS represents a significant advancement in personalized psychological care, providing a data-driven framework for understanding psychopathology symptoms, and implementing effective interventions.

Indexed as

Mental DisordersModels, PsychologicalPrecision MedicinePsychopathologyAdolescentFemaleHumansMaleUnited StatesHigh-dimensional spaceHiTOPP-factorPrecision psychiatryPsychopathology models

Identifiers

PMID41062532
PMCPMC12508039

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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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.