The effect of cognitive intervention in community senior citizens with mild cognitive impairment
Bibliographic record
Abstract
Objective To explore the effect of cognitive intervention in community senior citizens with mild cognitive impairment.Methods The samples were composed of 123 patients aged ≥60 years diagnosed with mild cognitive impairment.They were selected by cluster sampling in five communities of Shanghai Pudong New Area and were randomly divided into the cognitive intervention group(68 patients) and the control group(55 patients).The cognitive intervention was conducted in 96 sessions over 48 weeks.All individuals were assessed by mini mental status examination(MMSE),activities of daily living scale(ADL),Montreal cognitive assessment(MoCA) at baseline and end of intervention.Results After one-year intervention,the scores of MMSE,MoCA were higher in the intervention group than those in the control group(P0.05),there were no significant differences of ADL scores between the two groups.In the intervention group,the scores of MoCA and several its subitems including attention,Abstract ability and delay recall were significantly higher at the end of intervention than those before(P0.05).In the control group,the scores of MoCA and several its subitems including visuospatial function,attention,language,Abstract ability and delay recall were significantly lower at the end of study than those before(P0.05).Conclusions The cognitive intervention can improve cognitive functions in patients with mild cognitive impairment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".