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Record W6960052517 · doi:10.1192/j.eurpsy.2023.1048

General intelligence in adult patients with early- and adult-onset schizophrenia

2023· article· en· W6960052517 on OpenAlexaff

Bibliographic record

VenuePubMed Central · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetic and Environmental Crop Studies
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsIntelligence quotientSchizophrenia (object-oriented programming)Wechsler Adult Intelligence ScaleCognitionPsychosis

Abstract

fetched live from OpenAlex

INTRODUCTION: Early-onset schizophrenia (EOS) is a relatively uncommon disorder with psychotic symptoms emerging before 18 years of age. Although still under debate, EOS may be a more severe disorder relative to adult-onset schizophrenia (AOS), with worse prognosis. Cognitive deficits are a core feature of schizophrenia, accounting for a large part of the detrimental effect of the disorder and may reflect underlying neurodevelopmental disturbances. Some but not all previous studies show that the magnitude of cognitive deficits, including intelligence quotient (IQ), in patients with schizophrenia is dependent on the age of onset. OBJECTIVES: We aimed to assess IQ in adult patients with EOS and AOS, and healthy controls. We hypothesized that patients with EOS would show lower IQ than those with AOS, and both patient groups lower IQ than HC. METHODS: We included 136 adult patients with EOS (mean age: 24.7 (7.7) years, mean duration of illness: 9.3 (8.5) years, 50% women), 382 patients with AOS (mean age: 32.4 (9.5) years, mean duration of illness: 5.7 (6.6) years, 40.1% women) and 896 adult healthy controls (mean age: 33.2 (9.2) years, 47.1% women). We assessed current IQ with the Wechsler Abbreviated Scale of Intelligence (WASI) which yielded verbal (VIQ), performance (PIQ) and full-scale IQ (FIQ) scores. In a post-hoc analysis, we estimated premorbid IQ using the National Adult Reading Test (NART). We applied analyses of covariance (ANCOVAs) to investigate the putative differences in IQ scores and IQ change between patients with EOS, patients with AOS and healthy controls. RESULTS: In sex-, and age-adjusted models, FIQ and PIQ, but not VIQ, were significantly lower in EOS than in AOS (p=0.03, p<0.001 and p=0.428, respectively) (Image). Patients with EOS had fewer years of education than patients with AOS (p<0.001); the PIQ but not the FIQ difference between EOS and AOS remained significant after adjustment for education years (p=0.016 and p=0.333, respectively). Both patient groups had significantly lower IQ scores than healthy controls (Image). Further, patients with EOS and patients with AOS did not significantly differ in estimated premorbid IQ (109 and 110 units, respectively, p=0.092), whereas patients with EOS had a significantly larger estimated IQ decline after the disease onset compared to patients with AOS (12 and 9 units decline, respectively, p=0.015). Image: CONCLUSIONS: Our findings show that adult patients with EOS have significantly lower PIQ and FIQ scores, and significantly larger IQ decline after the disease onset, but not lower premorbid IQ, compared to patients with AOS. The adolescent onset of psychotic symptoms is linked, as expected, to fewer total years of education, which appears to explain the lower FIQ but only partially the lower PIQ in EOS, which may thereby be linked to the disorder per se. DISCLOSURE OF INTEREST: T. Calkova: None Declared, L. Mørch-Johnsen: None Declared, R. Elle Smelror: None Declared, K. Nordbø Jørgensen: None Declared, S. Cervenka: None Declared, K. Collste: None Declared, A. Vaskinn: None Declared, A. Margrethe Myhre: None Declared, O. A. Andreassen Consultant of: HealthLytix, Speakers bureau of: Lundbeck and Sunovion, T. Ueland: None Declared, I. Agartz: None Declared, D. Andreou: None Declared

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.169
Teacher spread0.160 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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