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 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".