Neurocognitive profiles in older adults with and without major depression
Bibliographic record
Abstract
OBJECTIVES: To delineate the differences between older persons with and without a diagnosis of major depression. METHODS: Participants were recruited from three outpatient clinics serving older patients at St Michael's Hospital. To be included in the study, participants had to speak English and have no evidence of significant sensory deficits that would interfere with neuropsychological testing. Participants were excluded if they had active delirium, active CNS disease (including dementia), active substance abuse, unstable medical disease, recent ECT treatment and a current/past diagnosis of a psychotic disorder. The diagnosis of major depression was made by qualified professionals in accordance with established guidelines. Participants were administered structured measures assessing global cognition, medical co-morbidity, subjective memory complaints, mood and detailed neurocognitive testing evaluating working memory, attention and speed of processing. Differences between depressed and non-depressed subjects with respect to these measures were analyzed using analysis of variance (ANOVA). RESULTS: Thirty-six participants were included in this study. The depressed (n = 17) and non-depressed (n = 19) groups were well matched in terms of age, education, medical co-morbidity and mini-mental state exam (MMSE) score. While the depressed subgroup had significantly higher subjective memory, language and cognitive complaints, there were no significant differences observed between the two subgroups on measures of memory and learning, attention and speed of information processing, fine motor dexterity and verbal fluency. CONCLUSION: This study suggests that while significant depressive symptoms are strongly associated with increased cognitive complaints, they are not associated necessarily with objective cognitive impairment.
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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 | 0.000 |
| 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".