The role of depression severity in the cognitive functioning of elderly subjects with central nervous system disease.
Bibliographic record
Abstract
OBJECTIVE: To examine the hypothesis that there is a causal relation between depression and cognitive dysfunction in patients with central nervous system (CNS) disease. DESIGN: Retrospective analysis of a clinical database. SETTING: Tertiary geriatric day hospital. PATIENTS: Sixty-five patients with depression and CNS disease, and 201 patients with depression but without CNS disease. OUTCOME MEASURES: Scores on the Hamilton Depression Rating Scale (Ham-D) and the Mattis Dementia Rating Scale (MDRS). RESULTS: A logistic regression analysis using MDRS status as the dependent variable, and a number of clinical variables as the predictor variables, showed that, in patients with CNS disease, only the Ham-D score predicted MDRS status (R = -0.19, p = 0.02). Ham-D score even more strongly predicted scores on a frontal system subtest of the MDRS (R = -0.262, p = 0.005). Ham-D score did not predict MDRS status in patients without CNS disease. Mean Mini Mental State Examination scores for the group with CNS disease were 25.1 at admission and 26.1 at discharge (p < 0.001). CONCLUSIONS: These findings suggest that depression contributes to frontal cognitive dysfunction in patients with CNS disease.
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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 | 0.006 |
| 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 | 0.001 | 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".