Testing the overlap between symptoms and cognition in schizophrenia: A multi-site study
Bibliographic record
Abstract
Background and Hypothesis. Cognitive impairment is a core feature of schizophrenia spectrum disorders (SSD). While the relationship between cognition and symptoms in SSD has been extensively studied, there is a lack of research focusing on individual item-level analysis rather than relying on summary score methodology. Our previous study on a first-episode psychosis cohort showed that symptoms related to impoverished and disorganized communication and motor impoverishment were associated with impairments in verbal and working memory. This study aimed to explore those associations in people with chronic schizophrenia.Study Design. We employed iterative Constrained Principal Component Analysis (iCPCA) to investigate the relationship between 15 cognitive measures, including processing speed, attention, working, verbal memory, reasoning, problem-solving and 26 PANSS items in 198 chronic schizophrenia outpatients from two sites in Australia and one in Canada. The iCPCA method was used to determine item combinations optimized to describe associations between symptoms and cognition while controlling Type I errors.Results. Our analysis revealed that a verbal memory component was associated with the PANSS item Lack of Spontaneity and Flow of Conversation, indicating impairments in communication, language, and thought. Additionally, a working memory and attention component was linked to the PANSS item Motor Retardation, reflecting motor system impoverishment.Conclusions. These results confirm our previous findings, showing that negative symptoms of diminished expression are key predictors of cognitive impairments in schizophrenia. These data underscore the value of optimized item combinations in identifying precise symptom-cognition relationships, which can potentially inform more effective treatment strategies.Keywords: schizophrenia, cognition, psychotic symptoms, verbal and working memory, motor impoverishment, disorganized and impoverished communication.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".