Evidence summary and evaluation on exercise intervention on mild cognitive impairment patients
Bibliographic record
Abstract
Abstract Objective:To obtain the guidelines,systematic review or metaanalysis related to exercise intervention on mild cognitive impairment(MCI),summarize and evaluate the evidence,so as to provide the reference for the development of recommendations for MCI patients′ relevant guidelines.Methods:Related guideliness and systematic reviews were retrieved from the Registered Nurses′Association of Ontario(RNAO),the American Academy of Neurology(AAN),the National Institute for Health and Clinical Excellence(NICE),Scottish Intercollegiate Guideline Network (SIGN),PubMed,Cochrane library,Web of science,CINAHL,China National Knowledge Infrastructure(CNKI),Wanfang Database,and VIP Database from January 2014 to February 2018.The quality of the literatures was evaluated using the AMSTAR 2 evaluation tool.If necessary,the GRADE tool was used to classify the evidence,and the quality of the guideline was evaluated using the AGREE II standard.Results:Finally,a guideline and 12 systematic reviews/metaanalysis were included in this study.AMSTAR 2 evaluation results showed that 9 articles were of medium quality.The GRADE evidence quality evaluation showed that 3 pieces of evidence were of high quality,11 pieces of evidence were of medium quality,10 pieces of evidence were of low quality,and 3 pieces of evidence were of very low quality.Conclusions:The current research results showed that exercise intervention had a good curative effect and could be used as a clinical adjuvant treatment for MCI patients.However,the system evaluation methodological quality of exercise intervention on MCI patients was generally lower,and its true effect needs a larger sample and further highquality research to explore.
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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 | 0.020 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.021 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".