Bibliographic record
Abstract
目的系统评价老年糖尿病患者衰弱的发生率,为制订适宜的护理干预措施提供依据。方法计算机检索PubMed、Embase、Web of Science、中国生物医学文献数据库、中国知网、维普和万方数据库,检索时限为建库至2020年8月,收集老年糖尿病患者衰弱发生率的观察性研究。由2名研究员根据纳入和排除标准独立筛选文献、提取资料,依据乔安娜布里格斯研究所(Joanna Briggs Institute,JBI)文献质量评价工具和纽尔卡斯-渥太华量表(Newcastle-Ottawa Quality Scale,NOS)对纳入文献进行质量评价,应用Stata 15.0软件进行系统评价,采用固定效应或随机效应模型进行数据合并,Egger秩相关检验和漏斗图判断发表偏倚。结果最终纳入27篇文献,共8 127例患者。系统评价结果显示,老年糖尿病患者衰弱的总体发生率为30%[95%CI(0.24～0.37)],衰弱前期的总体发生率为44%[95%CI(0.38～0.51)]。亚组分析结果显示,女性老年糖尿病患者衰弱的发生率(31%)高于男性(24%);医院发生率(31%)高于社区(30%);采用综合性衰弱筛查工具评估衰弱时,其发生率(43%)高于躯体性衰弱筛查工具(27%);北美洲地区发生率(41%)高于亚洲地区(28%)和欧洲地区(27%)。结论老年糖尿病患者衰弱的发生率较高,衰弱前期的发生率更高。护理人员不仅要重视对该人群进行衰弱的早期筛查,更要加强对衰弱前期的识别,制订合理的干预措施以改善老年糖尿病患者预后并提高其生活质量。
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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 | 0.023 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".