The association between sarcopenia and the physical function of patients with stroke: A systematic review and meta-analysis
Bibliographic record
Abstract
Objectives: The purpose of this study is to identify whether there is an association between sarcopenia and physical function outcomes of patients with stroke. Methods: A systematic search of Pubmed, Web of Science, Cochrane Library, Embase, China National Knowledge Infrastructure (CNKI), and Wanfang database was conducted to identify studies in Chinese and English from the inception of the database to March 2021. Documents were checked for relevancy. Articles exploring the association between sarcopenia and the physical function of patients with stroke were included. The quality of the literature was evaluated using the Newcastle-Ottawa scale tool. Stata 15.0 software was used to conduct meta-analysis. Results: Eight studies met the criteria for inclusion. A meta-analysis of four studies showed that sarcopenia was related to an increased risk of poor physical function of patients with stroke (total OR=3.11, 95% CI: 2.22-4.34, P<0.0001). Descriptive analysis was performed in the rest of studies. Overall, a correlation between the two factors was found in patients with stroke. Some studies suggested a difference based on gender and severity of the disease condition. The studies included in this review were of high methodological quality. The Egger's test (P=0.217) showed no publication bias. Conclusions: This review concludes that sarcopenia is an independent predictive factor of physical function of patients with stroke. Clinicians should pay attention to gender differences and severity of disease condition. Therefore, screening, diagnosis, treatment, and prevention of sarcopenia should be part of the routine clinical practice when providing care to stroke patients.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.032 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".