Cognitive dysfunctions in schizophrenia: potential benefits of cholinesterase inhibitor adjunctive therapy
Bibliographic record
Abstract
OBJECTIVE: In schizophrenia, cognitive dysfunctions commonly affect attention, memory and executive function, interfere with functional outcome and remain difficult to treat. Previous studies have implicated the cholinergic system in cognitive functioning. In Alzheimer's disease, cholinergic agonists have shown modest clinical benefits on cognitive and behavioural symptoms. Impaired cholinergic activity might also be involved in schizophrenia. Hence the role of cholinesterase inhibitors (ChEI) as adjunctive therapy is under study. We aimed to review the literature and evaluate the overall effectiveness of ChEI adjunctive therapy for the management of cognitive dysfunctions in schizophrenia. METHODS: We conducted a computer-based search using PubMed (up to February 15, 2006) and ISI Web of Science (conference proceeding abstracts from January 2003 to December 2005) databases. We used the search terms "schizophrenia," "cognition or memory" and "tacrine or donepezil or rivastigmine or galantamine." Studies included were critically analyzed for allocation, blindness, duration and study design, demographic data, and clinical and neuropsychological outcome assessments. We excluded studies that involved patients with psychiatric disorders other than schizophrenia-spectrum or if they involved animals or molecular investigations. We also excluded conference proceeding abstracts with no explicit neuropsychological battery and/or results. RESULTS: Data on ChEI as adjunctive therapy for the cognitive impairments in schizophrenia are sparse and so far derived from small samples and mostly open uncontrolled studies. ChEI's potential in long-term management has barely been documented and remains to be fully explored. CONCLUSION: There is insufficient evidence on whether ChEI should be used for the treatment of cognitive dysfunctions in schizophrenia. Nevertheless, further studies with appropriate trial designs and outcome measures in homogenous schizophrenia populations are warranted.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".