My patient might be depressed – can I still screen for MCI? Exploring cognitive performance on the MoCA in older people screened for depressive symptoms with the PHQ-9
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to compare the Montreal Cognitive Assessment (MoCA) performances of people who report no, subclinical, and clinical symptoms of depression. METHODS: Data was collected for the randomized controlled trial BrainFit-Nutrition. A secondary data analysis of 1,111 participants (age ≥ 60 years; M = 68.4 years; 55.1% female) was performed. Depressive symptoms were assessed with the Patient Health Questionnaire-9 (PHQ-9), cognitive performance was assessed via the MoCA. Performance differences were tested with Kruskal-Wallis tests. Two sensitivity analyses were conducted, one with data from people with MCI and one with the original item structure of the MoCA. RESULTS: No differences were found in the MoCA total score or in visuospatial, executive functioning, attention, memory, or orientation subscores between individuals with no, subclinical, or clinical symptoms of depression. A sensitivity analysis also showed no differences. CONCLUSION: Cognitive screening with the MoCA seems to be robust against depression and could therefore be used to screen for MCI regardless of depression level. TRIAL REGISTRATION: The study was prospectively registered at the International Standard Randomized Controlled Trial Number Registry on 23/11/2021 (ISRCTN 10560738).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".