Validation of the Italian version of Montreal Cognitive Assessment (MoCA) in routine clinical practice in community dweller older adults residing in the central area of Sicily
Bibliographic record
Abstract
Abstract Background/Objective: The aim of the study was to examine Montreal Cognitive Assessment (MoCA) performance in subjects with normal global cognition according to the Mini Mental State Examination (MMSE) in routine clinical practice. Methods: This was a prospective, clinical validation study in 302 consecutive subjects, referring to our dementia centre for suspected cognitive impairment over a six-month period. The MMSE and the MoCA were administered within two hours of each other. Results: 184 (60.91%) of 302 evaluated subjects with a MMSE score between 26 and 30/30 had a pathological MoCA score (< 26). 112/184 (60.84%) patients with a MMSE score between 26 and 29/30 obtained MoCA scores below the norm; 72/184 (39.13%) patients with a 30/30 MMSE had a MoCA score below the norm. Recall (p <.0001) and attention (p<.0001) were the domains that differed significantly on the two screening instruments. Conclusion: The additional use of MoCA, as a global assessment tool for the initial screening process, has allowed the identification of patients with cognitive deficit, despite their performance at MMSE had been the norm. Keywords: MoCA, MMSE, Cognitive Screening Test.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".