Comparison of the neurocognitive profiles of individuals with elevated psychotic or depressive symptoms
Bibliographic record
Abstract
AIM: Neurocognitive deficits are pervasive and enduring features of severe mental illness that appear before the onset of clinical symptoms and contribute to functional disability. However, it remains unclear how individuals who display warning signs for psychotic or mood disorders compare on their neurocognitive profiles since previous studies have separately examined neurocognition in both groups. Therefore, the purpose of this study was to directly compare performance on a range of neurocognitive tasks in individuals with emerging psychotic or mood symptoms. METHODS: Participants were drawn from a database of individuals who completed a comprehensive assessment at a university-based assessment centre. We examined 3 groups: individuals who endorsed elevated psychotic symptoms (EPS; n = 64), individuals who endorsed elevated depressive symptoms (EDS; n = 58), or non-clinical comparisons (NCC; n = 57) without any elevated psychiatric symptoms or diagnoses. RESULTS: EPS participants performed worse than NCC and EDS groups on verbal comprehension, working memory and cognitive flexibility, and worse than NCC, but not EDS, on perceptual reasoning. There were no significant differences between groups on processing speed, verbal fluency and set-shifting. EDS performed worse than both EPS and NCC groups on psychomotor speed. Dimensionally, poorer cognitive functioning was more strongly related to EPS than depressive symptoms. CONCLUSIONS: These findings highlight the distinct yet overlapping neurocognitive profiles of both groups with emerging psychiatric symptoms, and suggest that, despite having no formal diagnosis, individuals with EPS exhibit observable cognitive impairment and may still benefit from interventions within academic and workplace contexts.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".