Cognitive performance of long-term institutionalized elderly patients with schizophrenia: A case control study
Bibliographic record
Abstract
Cognitive impairment is inherent to the ageing process. Several studies suggest that patients with late-life schizophrenia have more marked cognitive impairment. OBJECTIVE: The aim of this study was to compare the cognitive performance of elderly institutionalized patients with schizophrenia and institutionalized elderly control patients without neurological or psychiatric diseases, matched for age, educational level and institutionalization time. METHODS: The Cambridge Examination for Mental Disorders of the Elderly (CAMCOG) was used to test 10 institutionalized elderly patients with schizophrenia. Results were compared with those of 10 institutionalized control patients with history of Hansen's disease. RESULTS: Patients with schizophrenia showed a worse performance in terms of total CAMCOG score and on its subtests of orientation, language, abstraction, and memory (p≤0.05). Patients with schizophrenia also disclosed a non-significant trend toward lower scores on the MMSE and on calculus. CONCLUSION: Findings demonstrated that schizophrenia was associated to worse cognitive impairment in long-term institutionalized elderly patients compared with institutionalized patients without neurological or psychiatric diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".