Neuropsychological deficits, syndromes, and cognitive competency in schizophrenia
Bibliographic record
Abstract
INTRODUCTION: Neuropsychological functioning connects neuropathology and symptoms in schizophrenia. Previous work suggests that deficient initiation and inhibition underlie the psychomotor poverty and disorganisation syndromes, respectively. Furthermore, although the syndromes are associated with impairments in everyday functioning, cognitive competency (CC; cognitive skills for independent living) has been neglected as an outcome. This study tested a three-level model of schizophrenia pathology (Neuropsychological dysfunction --> Syndromes --> CC), using unstructured neuropsychological tasks to measure initiation and inhibition. METHODS: Participants were 40 adults with schizophrenia. A verbal picture description and the Tinkertoy test yielded initiation and inhibition measures with good interrater reliability. Symptoms were rated using the SANS and SAPS, and an insight scale was administered. The Cognitive Competency Test utilised simulated situations to assess CC. RESULTS: Initiation failed to predict psychomotor poverty, but affected CC directly. Only one indicator of disinhibition (intermingling of personal material into speech) predicted disorganisation, which, through mediation, led to CC deficits. Insight correlated with disorganisation and contributed to CC. Unique effects of initiation, disorganisation, and insight, combined, explained 58% of CC variance. CONCLUSIONS: Partial support for the three-level model was obtained. Specific neuropsychological abilities and symptoms explain a substantial proportion of the variance in cognitive competency.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".