Enhanced computerized cognitive remediation therapy improved cognitive function, negative symptoms, and GDNF in male long-term inpatients with schizophrenia
Bibliographic record
Abstract
Objective: Negative and cognitive symptoms present significant challenges in patients with schizophrenia, and cognitive remediation is a promising approach to alleviate these symptoms. This study aimed to explore the efficacy of computerized cognitive remediation therapy (CCRT) on psychiatric symptoms, cognitive deficits, and serum levels of brain-derived neurotrophic factor (BDNF) and glial cell line-derived neurotrophic factor (GDNF) in patients with schizophrenia. Materials and methods: Forty male long-term institutionalized inpatients with schizophrenia were assigned to either a CCRT group (n = 20) or a control group (n = 20). The CCRT intervention consisted of 40 individual 40-min sessions over 8 weeks, conducted five times a week. Psychiatric symptoms, cognition, and serum levels of BDNF and GDNF were assessed at baseline, 4 weeks, and 8 weeks. Results: Compared to the control group, the CCRT group exhibited decreased total Positive and Negative Syndrome Scale and negative subscale scores, as well as increased Montreal Cognitive Assessment and Repeatable Battery for the Assessment of Neuropsychological Status scores. Moreover, improvements in list recall were associated with reduced negative symptoms. Additionally, CCRT ameliorated the decrease in serum GDNF levels in patients with schizophrenia. Conclusion: The effectiveness of CCRT in alleviating negative symptoms was associated with improvements in list recall, and GDNF may play a role in the observed effects of CCRT in patients with 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".