Can Cognitive Remediation in Groups Prevent Relapses?
Bibliographic record
Abstract
International guidelines define relapse prevention for schizophrenia patients as a key therapeutic aim. However, approximately 80% to 90% of schizophrenia patients experience further symptom exacerbation after the first episode. The purpose of this study was to investigate whether group integrated neurocognitive therapy (INT), a cognitive remediation approach, reduces relapse rates in schizophrenia outpatients. INT was compared with treatment as usual (TAU) in a randomized controlled trial. Fifty-eight stabilized outpatients participated in the study with 32 allocated to the INT group and 26 to the TAU group. A test battery was used at baseline, posttreatment at 15 weeks, and a 1-year follow-up. Relapse rates were significantly lower in the INT condition compared with TAU during therapy as well as at follow-up. The relapse rate after therapy was associated with significant reductions in negative and general symptoms, improvements in functional outcome, and overall cognition. Out of these variables, negative symptoms were identified to show the strongest association with relapses after therapy. The primary outcome of this study suggests that INT can prevent relapses in schizophrenia outpatients.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".