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Childhood adversity increases risk of psychotic experiences in patients with substance use disorder

2022· article· en· W4285735966 on OpenAlexaff
Ignacio Bórquez, Javiera Vásquez, Sofía Dupré, Eduardo A. Undurraga, Nicolás Crossley, Juan Undurraga

Bibliographic record

VenuePsychiatry Research · 2022
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsCanadian Institute for Advanced Research
FundersFondo Nacional de Desarrollo Científico y TecnológicoAgencia Nacional de Investigación y Desarrollo
KeywordsPsychiatryAdverse Childhood ExperiencesSubstance abuseSexual abuseMedicineClinical psychologyPsychologySuicide preventionPoison controlMental healthMedical emergency

Abstract

fetched live from OpenAlex

INTRODUCTION: Adverse childhood experiences (ACEs) increase the risk of psychotic experiences (PE), but little is known about heterogeneities of this association in different developmental stages, dimensions, or whether they are affected by substance use disorder (SUD). This study examines the association between different types of ACEs at various developmental stages and lifetime PE in patients with SUD in Chile. METHODS: We included 399 consenting adults in outpatient or residential SUD treatment programs. Sociodemographic data and information about PE and ACEs were obtained by trained clinical psychologists. RESULTS: Patients reporting PE experienced more ACEs compared to patients without PE (4.2 versus 3.4). They also experienced more complex adversities (41.8% versus 25.1%), had more psychiatric comorbidities (85% versus 70.4%), and reported using more substances (mean 4.5 versus 3.9). Adjusted association between ACEs and PE showed the highest OR for arrests (1.88), sexual abuse (1.81), alcohol abuse by parents (1.48), school exclusion (1.39), foster or residential care (18.3). CONCLUSION: Early exposure to ACEs is a risk factor for later PE among patients with SUD. Type of ACE and the period when they occurred is important, suggesting the existence of critical periods where the individual is more susceptible to adverse environmental stimuli.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.306
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2022
Admission routes1
Has abstractyes

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