Cognitive insight and verbal memory in first episode of psychosis
Bibliographic record
Abstract
Beck and collaborators have proposed a distinction between clinical insight and cognitive insight and have developed a tool for the assessment of the latter, namely the Beck Cognitive Insight Scale (BCIS). The present study explored in 51 patients with a first episode of psychosis the neurocognitive correlates of cognitive insight as assessed with the BCIS. Global measures for seven domains of cognition including verbal learning and memory, visual learning and memory, working memory, speed of processing, reasoning and problem solving, attention, and social cognition were examined. Secondly, we examined whether two clinical insight measures, the Scale to assess Unawareness of Mental Disorder (SUMD) and the insight item from the Positive and Negative Symptoms Scale (PANSS), could produce similar or different patterns of association with neurocognitive functions as those identified with the BCIS. Correlational analyses revealed significant associations between the BCIS Composite Index and the verbal learning and memory. No significant associations were observed between any of the neurocognitive domains and the PANSS or SUMD clinical insight measures, despite high inter-correlations among the three insight measures. These results suggest that cognitive insight, but not clinical insight, may rely on memory processes whereby current experiences are appraised based on previous ones.
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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 | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".