Cognitive Outcomes in Psychiatric Ward: Preliminary Results
Bibliographic record
Abstract
BACKGROUND: Studies conducted in high-income countries show that a significant proportion of people admitted to psychiatric wards have an underlying neurodegenerative disease (NDD) associated with psychiatric (PSY) disorders. In fact, neuropsychiatric symptoms are associated with neuroinflammation in the context of an NDD can often mimic psychiatric disorders, leading to misdiagnosis. This study aims to investigate features and patterns in cognitive performance associated with PSY and NDD conditions in individuals admitted to a psychiatric unit real-world clinical setting. METHOD: This is a prospective observational cohort study of patients admitted to a psychiatric ward. We assess functional status and determine whether individuals meet clinical diagnostic criteria for NDD during hospitalization. The target population includes patients over 45 years of age. Statistical analysis were performed by welch t-test and chi-squared test to estimate demographic differences between groups, and Analysis of Variance (ANOVA) for investigating cognitive domains within MoCA (Montreal Cognitive Assessment) test. RESULT: PSY group exhibits a higher proportion of women, a younger mean age, and a higher level of education compared to the NDD. Regarding cognitive performance, the PSY group demonstrates a higher mean MoCA total score (mean difference 4.8, 95% CI 1.45 to 8.06, p < 0.01). PSY group shows a lower mean score in the CDR assessment compared to the NDD group (mean difference -0.3, 95% CI -0.49 to -0.15, p < 0.01) (Table 1). Our results further demonstrate a significant difference in memory (mean difference 1.1, 95% CI 0.14 to 2.0, p < 0.05), language (mean difference 0,7, 95% CI 0.01 to 1.52, p < 0.05), and orientation (mean difference 0.8, 95% CI 0.30 to 1.27, p < 0.01) within MoCA domains between the groups (Figure 2). CONCLUSION: As expected, our study revealed that NDD group presents worse cognitive and functional performance compared to individuals with PSY illness. Regarding cognitive domains, we found a significant difference in memory, language, and orientation domains in the degeneration process. These results highlight the importance of specific assessments to characterize individuals admitted to psychiatric ward that will be better detailed in further analysis.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".