Exploration of risk factors for the mild cognitive impairment in retired veteran male
Bibliographic record
Abstract
Objective To study the influence of risk factors in retired veteran male with mild cognitive impairment(MCI).Methods 321 cases aged 60 and over of retired veteran male from 6 military sanatorium in Beijing city were studied.The score of mini mental state examination(MMSE),the Montreal cognitive assessment(MoCA),global deterioration scale(GDS),clinical dementia rating scale(CDR),activity of daily living(ADL),Hachinski ischemic scale(HIS),Hamilton depression scale and the risk factors were detected and analyzed.Results The prevalence of MCI was 25.7% in retired veteran male.The history of smoking and drinking,aging,hypertension,diabetes influenced the incidence of MCI(P 0.05).The hobby of exercise in last year,education level,hypoperfusion,insomnia,exposure to electromagnetic wave,mental scar history were not risk factors of MCI(P 0.05).Conclusions Aging and history of smoking,hypertension,diabetes may be the important risk factors for 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 | 0.000 |
| 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".