Differentiating the Cognitive Profile of Schizophrenia from That of Alzheimer Disease and Depression in Late Life
Bibliographic record
Abstract
BACKGROUND: To compare the cognitive profile of older patients with schizophrenia to those with other neuropsychiatric disorders assessed in a hospital-based memory clinic. METHODS: Demographic, clinical, and cognitive data of all patients referred to the memory clinic at the Centre for Addiction and Mental Health between April 1, 2006 and August 15, 2008 were reviewed. We then identified four groups of older patients with: (1) late-life schizophrenia (LLS) and no dementia or depression (DEP); (2) Alzheimer's disease (AD); (3) DEP and no dementia or LLS; (4) normal cognition (NC) and no DEP or LLS. RESULTS: The four groups did not differ in demographic data except that patients with AD were about 12 years older than those with LLS. However, they differed on cognitive tests even after controlling for age. Patients with LLS were impaired on most cognitive tests in comparison with patients with NC but not on recalling newly learned verbal information at a short delay. They experienced equivalent performance on learning new verbal information in comparison with patients with AD, but better performance on all other tests of memory, including the ability to recall newly learned verbal information. Finally, they were more impaired than patients with DEP in overall memory. CONCLUSIONS: Patients with LLS have a different cognitive profile than patients with AD or DEP. Particularly, memory impairment in LLS seems to be more pronounced in learning than recall. These findings suggest that cognitive and psychosocial interventions designed to compensate for learning deficits may be beneficial in LLS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".