Application of MoCA Scale in cognitive function assessment of healthy subjects
Bibliographic record
Abstract
Objective To study the application of MoCA Scale in cognitive function assessment of healthy subjects.Methods The cognitive function of 1350healthy subjects was assessed according to the MoCA Scale(Beijing version).Of the 857volunteers with their cognitive function assessed according to the the MoCA Scale(Beijing version),777were included in our analysis except for 80who were diagnosed with cognitive impairment.The 777subjects were divided into 65years old group(n=175),65-69years old group(n=200),70-74years old group(n=145), 75-79years old group(n=124),and≥80years old group(n=133),and into≤12years education group(n=153)and 13-20years education group(n=624).Results The total MoCA score was significantly different in different age groups and different education years groups(P 0.01).However,the time and place orientation score were significantly different in the other groups(P0.05).However,the naming and digital width score were significantly different in the other groups(P0.05).Multivariate linear analysis showed that age was negatively related with the total MoCA score(β=-0.639,P=0.000),education years were positively related with the total MoCA score(β=0.741,P=0.000).The MoCA score was≤25in65years old group,≤ 24in 65-69years old group,≤24in 70-74years old group,≤23in 75-79years old group,≤ 19in≥80years group,and≤20in≤12years education group,≤24in 13-20years education group.Conclusion The MoCA score of cognitive imairment is different in healthy subjects,attention should thus be paid to different individuals,especially to those with a lower education level and those at advanced age.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".