Cognitive performance in older elderly men with late-life depression and cardiovascular comorbidities: symptomatological correlation
Bibliographic record
Abstract
BACKGROUND: Whether depression or cardiovascular disease would have a greater effect on worsening cognitive impairment in the burgeoning older elderly population is uncertain. Which disorder causes greater cognitive impairment was investigated. METHODS: A cross section of 207 cognitively impaired older elderly (≥75 years old) men was recruited from outpatient clinics in southern Taiwan between 2004 and 2008. Their medical charts were reviewed for their history of medical illnesses, and those undergoing a current major depressive episode were screened using the Mini-International Neuropsychiatric Interview. Four groups of men were enrolled: 33 healthy controls (HC), 101 cognitively impaired patients with cardiovascular comorbidities (CVCs), 34 patients with late-life depression (LLD), and 49 patients with LLD and cardiovascular comorbidities (LLD + CVC). Several neuropsychological tests (e.g., Mini-Mental State Examination (MMSE), WCST, and Trail Making Test (TMT) parts A and B) were used to assess the participants. RESULTS: Cognitive function scores were highest in the HC group and lowest in the LLD + CVC group. There were no significant differences between the two groups with LLD comorbidity, and LLD was mostly associated with cognitive performance. LLD + CVC group members had the lowest recall memory, but their overall MMSE score was not significantly different. Moreover, this group had a higher but nonsignificantly different perseverative error than did the LLD group. Similarly, the LLD + CVC group was nonsignificantly slower at the TMT-A and TMT-B tasks than was the LLD group. CONCLUSIONS: LLD worsens neuropsychological function more than cardiovascular comorbidities do.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".