Cognitive Training in Older Adults with Mild Cognitive Impairment
Bibliographic record
Abstract
客观我们调查了为有温和认知缺陷(媒体控制接口) 的在农村背景并且与低教育层次的更老的成年人的认知训练的可行性和功效。方法 45 个更老的成年人(年龄 > 65 年) 与媒体控制接口被分到处理或控制组，在 2:1 比率。认知训练发生在处理组 2 个月。参加者的认知能力在预先训练，中期，和 training 以后时间点被估计，用微型心理的州的考试(MMSE ) ，蒙特利尔认知评价(MoCA ) ， Loewenstein 职业治疗认知评价(LOTCA ) ，和哈密尔顿消沉可伸缩(HAM-D ) 。跟随训练、认知能力的结果在处理组改善了，基于所有 4 项措施的全部的分数，以及明确地在 MoCA 和 LOTCA 上。在某 subscales 上在组和时间点的主要效果有差别，但是如果有的话，几乎没有的这些差别在全面分析上完成。现在的学习表明了那认知训练的结论在注意，语言，取向，视觉感觉，视觉运动的组织，并且与媒体控制接口的在病人的逻辑询问上有有益的效果。而且，观察效果是长期的变化。
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".