Impact of sarcopenia on the risk of falls, osteoporosis, fractures, and all causes of death among elderly people: A Meta-analysis of prospective cohort studies
Bibliographic record
Abstract
Objective To explore the relationship between sarcopenia and the risks of falls, osteoporosis, fractures and all-cause mortality among elderly people. Methods This was a meta-analysis of prospective cohort studies. Databases of OVID/Medline, PubMed, EMBASE, Cochrane Library, China National Knowledge Infrastructure (CNKI) and Chinese WanFang Database were searched systematically according to the inclusion and exclusion criteria.The literatures related to the relationship between sarcopenia and falls, osteoporosis, fractures and all-cause mortality among elderly people from January 1987 to June 2017 were identified.The quality of the literature was evaluated by the risk assessment tool Newcastle-Ottawa Scale recommended by the Cochrane. Meta-analysis was conducted by RevMan 5.3 and Stata 12.1 software. Results Totally 13 prospective cohort studies including 19 376 subjects and 3 190 outcome events were entered in meta-analysis. The relative risk (RR) for comprehensive adverse outcome events among subjects with sarcopenia was 1.64 times of non-sarcopenia subjects(95% CI=1.51-1.78, P<0.000 01), and the RRs for fall, osteoporosis, fractures and all-cause mortality were 1.60 (95% CI=1.42-1.81, P<0.000 01), 4.85 (95% CI=2.18-10.79, P=0.000 1), 1.59 (95% CI=1.40-1.80, P<0.000 01), 2.08 (95% CI=1.18-3.69, P=0.01) times of non-sarcopenia subjects respectively. Conclusion Sarcopenia increases the risk of falls, fractures, all-cause mortality and comprehensive adverse outcome significantly, suggesting that sarcopenia might be a predictor for adverse outcomes among elderly people. Key words: Sarcopenia; Falls; Osteoporosis; Fractures; Mortality
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.072 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".