Relationship of Cognition to Clinical Response in First-Episode Schizophrenia Spectrum Disorders
Bibliographic record
Abstract
First-episode schizophrenia (FES) spectrum disorders are associated with pronounced cognitive dysfunction across all domains. However, less is known about the course of cognitive functioning, following the first presentation of psychosis, and the relationship of cognition to clinical course during initial treatment. The present longitudinal study examined the magnitude of neurocognitive impairment, using the MATRICS Consensus Cognitive Battery, in patients experiencing their first episode of psychosis at baseline and after 12 weeks of randomized antipsychotic treatment with either aripiprazole or risperidone. At baseline, FES patients evidenced marked impairments in cognitive functioning. Notably, performance on the mazes task of planning and reasoning significantly predicted the likelihood of meeting stringent criteria for positive symptom remission during the first 12 weeks of the trial. Performance on indices of general cognitive function, working memory, and verbal learning improved over time, but these improvements were mediated by improvements in both positive and negative symptoms. We did not detect any differential effects of antipsychotic medication assignment (aripiprazole vs risperidone) on cognitive functioning. Our results suggest that a brief paper-and-pencil measure reflecting planning/reasoning abilities may index responsivity to antipsychotic medication. However, improvements in cognitive functioning over time were related to clinical symptom improvement, reflecting "pseudospecificity."
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".