Overlap between individual differences in cognition and all SANS and SAPS symptoms of schizophrenia
Bibliographic record
Abstract
Background and Hypothesis. Neurocognitive impairment is a core feature of schizophrenia spectrum disorders (SSDs), and the relationship between cognition and symptoms in SSDs is widely researched. The most well-replicated finding is an association between episodic memory and negative symptoms; however, the aspects of negative symptoms that underpin this relationship have yet to be specified.Study Design. We use iterative Constrained Principal Component Analysis (iCPCA) to explore the relationship between cognition and symptoms in schizophrenia at the level of individual items while minimizing the risk of Type I errors. ICPCA was conducted on a sample of SSD patients in the early stages of psychiatric treatment (n = 206) to determine the components of cognition overlapping with symptoms measured by the Scale for the Assessment of Negative Symptoms (SANS) and the Scale for the Assessment of Positive Symptoms (SAPS).Results. We found that a verbal memory component was associated with items from both SANS and SAPS related to disorganized and impoverished communication, language and thought (including both positive and negative formal thought disorder). In contrast, a working memory component was associated with SANS items related to motor system impoverishment. Both were related to social/clinical inattentiveness.Conclusions. ICPCA allows for a finer-grained analysis of the aspects of the illness responsible for the overlap between cognition and symptoms, made possible by analyzing individual items instead of symptom summary scores. These results suggest that verbal and working memory systems overlap with different aspects of SSD symptomatology, implying distinct brain networks underpinning these relationships.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".