Smell test predicts performance on delayed recall memory test in elderly with depression
Bibliographic record
Abstract
INTRODUCTION: Elderly with depression are at increased risk for cognitive dysfunction and dementia. Smell tests are correlated with performance on cognitive tests in the elderly and therefore might serve as a screening test for cognitive impairment in depressed elderly. PURPOSE: To assess the validity of the CC-SIT (Cross-Cultural Smell Identification Test) as a screening test for cognitive impairment in elderly with depression. METHODS: Forty-one patients, aged 60 and over, were assessed with the CC-SIT and CVLT (California Verbal Learning Test) after 3 months treatment of a Major Depressive Episode (DSM-IV) at the Day Hospital for Depression, Baycrest. Patients already diagnosed with dementia, or other psychiatric and neurological disorders, were excluded. Receiver Operating Characteristics (ROC) analysis was applied to assess the CC-SIT's accuracy in identifying individuals with impairment (2 SD below the mean for age and education or less) on CVLT delayed recall trials. RESULTS: Forty-one patients (33 women and eight men) were assessed. Mean age was 76.8 (SD: 6.5), mean HRSD scores before treatment was 22.0 (SD: 5.1). Nine patients had impairment on CVLT delayed recall measures. The area under the ROC curve was 0.776 (95% CI = 0.617-0.936). CONCLUSIONS: Our results support the use of the CC-SIT as a screening tool for cognitive impairment among elderly with depression as an indicator for the need of a comprehensive neuropsychological evaluation. Replication with larger samples is necessary.
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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 | 0.004 |
| 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".