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Record W2525353123 · doi:10.1192/bjp.bp.116.187682

Psychosis in autism: Comparison of the features of both conditions in a dually affected cohort

2016· article· en· W2525353123 on OpenAlexfundno aff
Felicity V. Larson, Adam P. Wagner, Peter B. Jones, Digby Tantam, Meng‐Chuan Lai, Simon Baron‐Cohen, Anthony Holland

Bibliographic record

VenueThe British Journal of Psychiatry · 2016
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsnot available
FundersMenzies Centre for Australian Studies, King's College London, University of LondonMedical Research CouncilHospital for Sick ChildrenUniversity of OxfordKing's College LondonNational Institute for Health and Care ResearchCentre for Addiction and Mental Health
KeywordsPsychosisAutismCohortPsychiatryPsychologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

Background There is limited information on the presentation and characteristics of psychotic illness experienced by people with autism spectrum disorder (ASD). Aims To describe autistic and psychotic phenomenology in a group of individuals with comorbid ASD and psychosis (ASD–P) and compare this group with populations affected by either, alone. Method We studied 116 individuals with ASD–P. We compared features of their ASD with people with ASD and no comorbid psychosis (ASD–NP), and clinical characteristics of psychosis in ASD–P with people with psychosis only. Results Individuals with ASD–P had more diagnoses of atypical psychosis and fewer of schizophrenia compared with individuals with psychosis only. People with ASD–P had fewer stereotyped interests/behaviours compared with those with ASD–NP. Conclusions Our data show there may be a specific subtype of ASD linked to comorbid psychosis. The results support findings that psychosis in people with ASD is often atypical, particularly regarding affective disturbance.

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 score0.062
Threshold uncertainty score0.349

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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.312
Teacher spread0.296 · 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

Citations124
Published2016
Admission routes1
Has abstractyes

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