Cognitive remediation in schizophrenia: efficacy and effectiveness in patients with early versus long‐term course of illness
Bibliographic record
Abstract
AIM: We examined the efficacy and effectiveness (transfer to functional competence and everyday functioning) of cognitive remediation in early-course (within 5 years of first episode) and long-term (more than 15 years of illness) schizophrenia. METHODS: Treatment lasted 12 weeks and included computerized exercises, strategic monitoring and methods to transfer cognition to behaviour. Assessments included a standard battery of neurocognition, performance-based measures of social and adaptive competence, and case manager ratings of real-world functional behaviour. Changes from baseline to post-treatment were examined with repeated measures analysis of variance and estimated premorbid intelligence and total months in hospital as covariates. RESULTS: The early-course group had larger improvements in measures of processing speed and executive functions, as well as larger improvements in adaptive competence and real-world work skills. Duration of illness was inversely associated with improvement in neurocognition and real-world work skills. CONCLUSIONS: Treatment of cognitive impairments is feasible in both early-course and chronic schizophrenia, but the clinical meaningfulness and generalization to functioning appear to be more substantial when delivered early. Cognitive remediation should be considered a tool for early intervention in schizophrenia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".