Japanese version of the <scp>Montreal Cognitive Assessment</scp> cut‐off score to clarify improvement of mild cognitive impairment after exercise training in community‐dwelling older adults
Bibliographic record
Abstract
AIM: Physical exercise improves cognitive function in people with mild cognitive impairment (MCI). However, information about whether the degree of MCI before exercise training affects improvement in cognitive function is lacking. Therefore, we aimed to investigate the cut-off value in a MCI screening tool that predicts reversal to normal cognitive function after exercise training in older adults with MCI. METHODS: Participants included 112 Japanese community-dwelling older adult outpatients (37 men, 75 women; mean age 76.3 years). We administered the Japanese version of the Montreal Cognitive Assessment (MoCA-J) before and after exercise training. MCI was defined as a MoCA-J score <26. All participants underwent exercise training 2 days per week for 6 months, according to American Heart Association guidelines. RESULTS: The prevalence of MCI was 65.2%. After exercise training, 46.6% of participants with MCI reversed to normal cognitive function. The MoCA-J cut-off score to predict cognitive function potentially reversible to normal was 23, with receiver operating characteristic analysis showing an area under the curve of 0.80, sensitivity of 79.4% and specificity of 69.2%. Multiple logistic regression analysis to predict non-MCI after exercise training showed that MoCA-J score ≥23 (OR 6.9, P < .001), female sex (OR 3.4, P = .04) and age (OR 0.9, P = .04) were independent determinants. CONCLUSIONS: The MoCA-J cut-off score of 23 might be useful to predict cognitive function that is potentially reversible to normal among community-dwelling Japanese older adults with MCI. Geriatr Gerontol Int 2018; 18: 833-838.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".