Application and Revision of Montreal Cognitive Assessment in China's Military Retirees with Mild Cognitive Impairment
Bibliographic record
Abstract
OBJECTIVE: In an effort to accommodate MOCA to better fit for the Chinese context, this study was designed to employ the MOCA criteria to screen mild cognitive impairment (MCI) and analyze associated risk factors in military retirees. METHODS: Three hundred and four retired military cadres were recruited using a random cluster sampling technique with information collected including personal, prevalence, MOCA scale, and related neuropsychiatry scale. Thirty retirees were randomly chosen to be further analyzed one month later using the revised MOCA scale. RESULTS: ①Our data indicated an incidence rate of 64.8% for mild cognitive impairment in retired military cadres. The incidence rate for MCI was significantly higher in those aged 80 or above compared with those 80 years of age or younger (P<0.05). The incidence rate of MCI was significantly higher in those with fewer than 6 years of education compared with those with over 7 years of education (P<0.05). The MCI incidence was higher for those with little exercise than those taking regular exercise (P<0.01). Moreover, the MCI incidence was higher in stroke patients than those who never had a stroke episode (P<0.05). ②There was a significant correlation between MOCA and MMSE scale scores (r = 0.81). MOCA scale scores were negatively correlated with ADL and CES-D scores (although not PSQI scores). ③ MOCA recension Cronbach's alpha value was 0.862. The related coefficient of MOCA and MOCA recension was 0.878(P<0.01). When the Score of cut-off -point of the MOCA recension was 28, the area in ROC curve analyses was 0.859, as well as the largest area. CONCLUSION: Retired cadres exhibited a greater incidence of MCI (than general population), which was closely associated with age, level of education and physical exercise and cerebral apoplexy. Revised MOCA scale displays a better validity and reaction degree of reliability and is more suitable for screening and diagnosis of MCI in the elderly in China.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".