Impact of psychological distress on the Montreal cognitive assessment (MOCA) among geriatric outpatients
Bibliographic record
Abstract
Geriatric patients often present with multiple and occasionally complex diseases compared to younger patients. This poses a problem to clinicians and other health care providers who must disentangle the comorbidities in order to interpret screening results accurately, diagnose disease type correctly and select treatment plan accordingly. In particular, performance on brief cognitive screening tests, such as the Montreal Cognitive Assessment (MoCA), may be influenced by the presence of clinically significant levels of depressive and anxiety symptoms. Hence, this cross-sectional study aims to assess whether presence of clinically significant levels of depressive or anxiety symptoms impact probability of success on specific MoCA questions among geriatric outpatients. Participants were recruited from two geriatric outpatient clinics in Montreal and enrolled participants were administered cognitive, depression and anxiety screening tests. Comparison of MoCA performance between low versus high levels of depression or anxiety symptoms was analyzed within a Rasch model framework via Differential Item Functioning (DIF) analysis. The results reveal that the probability of correctly answering a specific MoCA item is not influenced by the presence of clinically significant depressive or anxiety symptoms for all items on the MoCA. The present study’s finding is clinically and practically applicable because it can be generalized to similar geriatric outpatient clinic settings, however further research is needed to investigate whether these findings are comparable among patients with formal psychiatric diagnoses. In conclusion, the MoCA can be used to screen for cognitive impairment amongst the general population of geriatric outpatients, regardless of recent depression and anxiety status.
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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 | 0.002 | 0.017 |
| 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 | 0.001 |
| 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".