Application of Montreal Cognitive Assessment to cognitive function evaluation in the old-elderly
Bibliographic record
Abstract
Objective To explore the application of Montreal Cognitive Assessment(MoCA) to cognitive impairment evaluation in the old-elderly.Methods One hundred and seventeen retired residents aged 80 years or older were recruited and both MoCA and Mini-Mental State Examination (MMSE) were conducted to assess their cognitive conditions,then the results of both scales for subjects with different educational levels,different ages and different genders were analyzed with SPSS software.Results MoCA scores ranged from 4 to 29(22.72±4.35),MMSE scores ranged from 5 to 30(26.35±3.30).The overall scores of MoCA were lower than those of MMSE.Additionally,MoCA scored less than MMSE for different educational levels,different ages as well as for different genders.The score differences between the two scales were statistically significant(P0.05,P0.01).The items of MoCA sorted in descending order on the basis of the damage degree were as follows;delayed recall,visuospatial and executive function,language, abstract thinking,orientation,naming,calculation,attention.Age(β=-0.293,P=0.001) and educational level(β=0.685,P=0.009) were influential factors for MoCA scores.Conclusion MoCA is applicable to evaluating cognitive impairment of the old-elderly and is better than MMSE in diagnosis of cognitive impairment.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".