Muscle Weakness and Falls in Older Adults: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVES: To evaluate and summarize the evidence of muscle weakness as a risk factor for falls in older adults. DESIGN: Random-effects meta-analysis. SETTING: English-language studies indexed in MEDLINE and CINAHL (1985-2002) under the key words aged and accidental falls and risk factors; bibliographies of retrieved papers. PARTICIPANTS: Fifty percent or more subjects in a study were aged 65 and older. Studies of institutionalized and community-dwelling subjects were included. MEASUREMENTS: Prospective cohort studies that included measurement of muscle strength at inception (in isolation or with other factors) with follow-up for occurrence of falls. METHODS: Sample size, population, setting, measure of muscle strength, and length of follow-up, raw data if no risk estimate, odds ratios (ORs), rate ratios, or incidence density ratios. Each study was assessed using the validity criteria: adjustment for confounders, objective definition of fall outcome, reliable method of measuring muscle strength, and blinded outcome measurement. RESULTS: Thirty studies met the selection criteria; data were available from 13. For lower extremity weakness, the combined OR was 1.76 (95% confidence interval (CI)=1.31-2.37) for any fall and 3.06 (95% CI=1.86-5.04) for recurrent falls. For upper extremity weakness the combined OR was 1.53 (95% CI=1.01-2.32) for any fall and 1.41 (95% CI=1.25-1.59) for recurrent falls. CONCLUSION: Muscle strength (especially lower extremity) should be one of the factors that is assessed and treated in older adults at risk for falls. More clinical trials are needed to isolate whether muscle-strengthening exercises are effective in preventing falls.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.032 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".