Cognitive impairment in adolescent and adult-onset psychosis: a comparative study
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment presents in both adolescent-onset(ado-OP) and adult-onset psychosis(adu-OP). Age and neurodevelopmental factors likely contribute to cognitive differences. This study aimed to characterize cognitive functions in ado-OP compared to adu-OP in a clinical population with drug-naive first-episode psychosis(FEP). METHODS: A total of 788 drug-naive patients with FEP and 774 sex- and age-matched healthy controls(HCs) were included. Participants were divided into four groups by whether they were under or over 21 years of age: adolescent-onset FEP(ado-FEP, n = 380), adult-onset FEP(adu-FEP, n = 408), adolescent HC(ado-HC, n = 334), and adult HC(adu-HC, n = 440). Comprehensive cognitive assessments were performed using the MATRICS Cognitive Consensus Battery(MCCB), covers six cognitive domains: speed of processing, attention/vigilance, working memory, verbal learning, visual learning, reasoning, and problem-solving. Data analyses were conducted using correlation analyses and binary logistic regression. RESULTS: The patterns of cognitive domain differences between ado-FEP and adu-FEP were found to be similar to those between ado-HC and adu-HC, whereas cognitive impairments appeared to be more pronounced in patients with adu-OP than ado-OP. The mazes subtest had the maximum effect size(ES) in the FEP(ES = 0.37) and HC(ES = 0.30) groups when comparing the adolescent and adult groups. Cognitive subtests were mostly significantly correlated with negative symptoms, especially for adolescents with FEP, in which all the subtests were significantly correlated with negative symptoms in the ado-FEP group. Better performance in the domains of spatial cognition and problem-solving abilities was more likely in the ado-FEP group than in the adu-FEP group. CONCLUSIONS: These findings suggest cognitive differences between adolescents and adults but similar patterns of affected domains in HCs and patients with FEP. Therefore, the development of targeted cognitive interventions tailored to the specific needs of different age groups appears warranted.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".