Application of Montreal Cognitive Assessment for Screening MCI in Community Elderly in Chengdu
Bibliographic record
Abstract
目的:本文旨在研究蒙特利尔认知评估量表(MoCA)在成都市社区老年人轻度认知功能障碍(MCI)筛查中的价值,探讨该量表对社区老年人MCI筛查的最佳分界值。方法:采用简易精神状态量表(MMSE)和MoCA对成都市社区老年人进行MCI筛查,计算MoCA的信度、效度、敏感性、特异性和Youden指数,并计算适合本市老年人MCI患者的划界分。结果:参与此次社区调查并配合完成所有测试的人数为674人,其中MCI患者106人,MoCA量表的Cronbach’sα为0.852;其总分与MMSE相关系数为0.9392;MoCA以原版推荐26分为界,对MCI筛查的敏感性和特异性分别为98.11%和26.72%,Youden指数为0.2483。结论:用MoCA对成都市社区老年人认知功能的筛查是简便可行的,具有良好的信度、效度和敏感性。推荐以22分作为我市社区老年人的MCI分界值。
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".