Investigating cognitive deficits and symptomatology across pre-morbid adjustment patterns in first-episode psychosis
Bibliographic record
Abstract
BACKGROUND: Cognitive deficits in schizophrenia are well established and are known to be present during the first episode of a psychotic disorder. In addition, consistent heterogeneity within these impairments remains unexplained. One potential source of variability may be the level of pre-morbid adjustment prior to the onset of first-episode psychosis (FEP). METHOD: Ninety-four FEP patients and 32 healthy controls were assessed at baseline on several neuropsychological tests comprising six cognitive domains (verbal memory, visual memory, working memory, processing speed, reasoning/problem-solving and attention) and an abbreviated version of the full IQ. A global neurocognitive domain was also computed. Pre-morbid adjustment patterns were divided into three distinct groups: stable-poor, stable-good and deteriorating course. RESULTS: Based on a cut-off of 0.8 for effect size, the stable-poor pre-morbid adjustment group was significantly more impaired on most cognitive domains and full IQ compared to the deteriorating group, who were more severely impaired on all measures compared to the stable-good group. The type of cognitive deficit within each subgroup did not differ and the results indicate that a global neurocognition measure may reliably reflect the severity of cognitive impairment within each subgroup. CONCLUSIONS: Pre-morbid adjustment patterns prior to onset of psychosis are associated with severity but not type of cognitive impairment. Patients in the stable-poor group are generally more impaired compared to the deteriorating group, who are, in turn, more impaired than the stable-good group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".