Status quo of mild cognitive impairment and its influencing factors among elderly people in nursing institutions for the aged
Bibliographic record
Abstract
Abstract Objective:To investigate the prevalence and influencing factors of mild cognitive impairment(MCI)of elderly people in old people′s homes in Nanjing.Methods:Above the 80-year-old old people were selected by cluster sampling in 10 endowment institutions of Nanjing from June to July 2017.Taking the way of questionnaire survey combined with observation,the survey was carried out by self-made elderly general situation questionnaire,the Montreal Cognitive Assessment Scale,Tilburg Frailty Indicator,and Berg Balance Scale,Barthel Index Scale and time up and go test.Results:A total of 218 elderly people were investigated,among whom 90 were MCI,accounting for 41.3%.Binary Logistic regression analysis showed that education background,frailty and balancing function were the influencing factors of mild cognitive impairment in the elderly.Conclusions:The elderly with low education level,poor balancing function and mobility are high-risk groups for MCI.Early screening and identification should be strengthened and active intervention should be carried out in order to slow down the occurrence and development of frailty and 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 | 0.001 |
| Science and technology studies | 0.001 | 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".