Summary of best evidence for Tai Chi exercise management in older adults with chronic diseases
Bibliographic record
Abstract
Objective To systematically screen, extract and summarize the best evidence of Tai Chi exercise management for older adults with chronic diseases, providing evidence-based basis for clinical development of standardized, scientific, effective and comprehensive Tai Chi management programs. Methods BMJ Best Practice, Registered Nurses’ Association of Ontario(RNAO), UpToDate, Guidelines International Network(GIN), National Institute for Health and Care Excellence(NICE), Scottish Intercollegiate Guidelines Network(SIGN), National Guideline Clearinghouse(NGC), New Zealand Guidelines Group(NZGG), Yimaitong, JBI Library, Cochrane Library, Web of Science, PubMed, CINAHL, SinoMed, Wanfang Database and CNKI were systematically searched from inception to August 2023, including clinical practice guidelines, expert consensuses, clinical decisions, evidence summaries and systematic reviews of Tai Chi exercise management for older adults with chronic diseases. Two researchers independently conducted literature screening and quality evaluation, and extracted, summarized and analyzed the evidence. Results A total of 32 literatures were involved, including 2 expert consensus and 29 systematic reviews, which were summarized in 9 aspects of exercise benefits, applicable objects, influencing factors, exercise environment, exercise programs, exercise types, exercise doses, exercise compliance and exercise safety, and 39 pieces of evidence. Conclusion Healthcare professionals should comprehensively consider the characteristics and preferences of older adults with different chronic diseases based on the clinical context, and carry out evidence transformation and application of Tai Chi management programs, so as to improve the quality of life of older people and promote healthy aging. (目的 系统检索、筛选、提取和整合老年慢性疾病患者太极拳运动管理的最佳证据总结, 为临床制定统一、规范、科学、有效、全面的老年慢性疾病患者太极运动管理方案提供循证依据。方法 计算机检索BMJ最佳临床实践、加拿大安大略注册护士协会指南网、UpToDate、指南国际网络、英国国家卫生与临床优化研究所指南网、苏格兰学院间指南网、美国国家指南库、新西兰指南协作组、医脉通、JBI图书馆、Cochrane图书馆、Web of Science、PubMed、CINAHL、中国生物医学文献数据库(CBM)、万方数据库(Wanfang Data)、中国知网(CNKI), 包括临床实践指南、专家共识、临床决策、证据总结、系统评价等。检索时间从建库至2023年8月。由2名研究人员独立完成文献筛选和质量评价, 对证据进行提取、汇总和分析。结果 共纳入31篇文献, 包括专家共识2篇和系统评价29篇, 从运动效益、适用对象、影响因素、运动环境、运动方案、太极类别、运动剂量、运动依从和运动安全9个方面总结了老年慢性疾病患者太极运动管理相关证据共39条。结论 医养护专业人员应结合临床情境, 综合考虑不同慢性疾病老年患者的特点和偏好, 进行老年慢性疾病患者太极拳运动管理方案的证据转化应用, 以改善老年慢性疾病患者的生活质量, 促进健康老龄化。)
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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 | 0.012 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.020 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".