Study of Prevalence of the Mild Cognitive Impairment(MCI) and the Associated Factors Among the Elderly in Hospital
Bibliographic record
Abstract
Objective:To investigate the prevalence of the mild cognitive impairment(MCI) among elder people in hospital and the associated factors to it.Methods:From January to June in 2012,to investing the elderly people aged 60 or above in hospital.First face-to-face interview to the subjects was performed by trained interviewers to conduct general questionnaire and mini mental examination(MMSE) and Montreal cognitive assessment(MOCA).Second,for subjects with complaints or distinct cognitive impairment and those with the score less than the cut-off point of MMSE.The test were conducted which include physical examine,Global Deterioration Scale,Hachinski Ischemic Scale,activities of daily living scale(ADL).Then MCI was diagnosed by the consensus of two older physicians.Results:A total of 218 subjects include 107 males and 111 females.Among the subjects,59(27.07%)were defined to have MCI.The prevalence of MCI have significances with different sex,ages,educations and occupations(P0.05),the mean plasma level HCY was higher in MCI patients than that in contrast group.Conclusion:The prevalence of MCI was 27.07% among the elderly in hospital.The seniors,the less educated,the manual workers and the females were high risk group of MCI.The high level of plasma HCY is an important risk factor to MCI patients.
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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".