Factors affecting cognitive remediation outcome in schizophrenia: The role of treatment resistance
Bibliographic record
Abstract
Introduction Treatment-resistant schizophrenia (TRS) represents a major clinical issue, characterized by worse psychopathological outcome, a more disrupted neurobiological substrate and higher healthcare costs. Cognitive impairment is a core feature of schizophrenia, strongly associated with patients’ functional outcome. Different studies showed that TRS patients exhibit poorer neurocognitive performance, particularly on verbal domains. To date Cognitive Remediation Therapy (CRT) represents the best available tool for treating cognitive deficits in schizophrenia. However, CRT outcomes are highly heterogeneous and significant treatment predictors are still lacking. Objectives To investigate possible differences of CRT outcome among patients with schizophrenia, stratified according to antipsychotic response (TRSs vs. first-line responders - FLRs). Methods 150 patients with schizophrenia, (95 FLRs, 55 TRSs) were assessed for neurocognition with BACS and WCST at baseline and after CRT. General Linear Models (GLMs) were performed to investigate possible differences between groups on basal cognition and CRT outcome (Cohen’s d Effect Size). Results At baseline, GLMs showed significant differences in Verbal Memory (F=4,66; p=0,03) and WCST–executive functions (F=5,59; p=0,02), both worse in TRS group. Effecr Sizes of CRT outcome resulted significantly different in domains of Verbal Memory (F=4,68; p=0,03) and WCST–executive functions (F=4,62; p=0,03), with greater improvements among TRS patients. Conclusions This is the first study to indicate treatment-resistance as a possible predictor of CRT outcome in schizophrenia. Moreover, we observed that CRT resulted able to fill the cognitive gap between treatment groups. Thus, these results further highlight the importance of early cognitive interventions in order to reduce the neuropsychological and functional burden associated with the disease, especially for TRS patients. Disclosure No significant 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".