Exploration of High Risk Factors in Lifestyle for Mild Cognitive Impairment in Elderly People
Bibliographic record
Abstract
Objective:To explore the influence of risk factors in lifestyle to elderly mild cognitive impairment.Methods:With the score of(mini-mental state examination) MMSE,(Montreal Cognitive Assessment) MoCA,(Clinical dementia rating scale) CDR and the risk factors of life scale,the study investigated 219 elderly people aged over 60 years to analyze high risk factors in lifestyle of mild cognitive impairment.Results:The male was higher than the men in the risk of MCI(P = 0.03);The illiteracy group was higher than the Primary school group in the risk of MCI,middle school and above group was higher than the Primary school group in the risk of MCI(P = 0.00);The farmer group was higher than the worker group in the risk of MCI;The worker group was higher than the management group in the risk of MCI(P = 0.01);The rural residents were higher than the urban residents in the risk of MCI(P = 0.01);The exercise influenced the incidence of MCI.Exercise group was higher than the non-exercise group in the risk of MCI(P =0.00);The hobby influenced the incidence of MCI,A hobby group was higher than the non-hobby group in the risk of MCI(P = 0.00).Age was no significant difference in the risk of elderly MCI(P 0.05);The groups between different smoking,drinking,drinking tea(the history of years,frequency,etc.) were no significant difference in the risk of elderly MCI(P 0.05);Whether the lunch break or not,different frequency of lunch break and different sleeping hours per night were no significant different in the risk of elderly MCI(P 0.05).Conclusions:gender,education level,occupation,place of residence,exercise and hobbies and so on had something to do with the risk of elderly MCI.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".