Preventing Falls in Older Adults After Upper Limb Fractures: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: The objective was to identify and describe fall prevention strategies in upper limb fracture rehabilitation for older people using recent fall prevention guidelines as a standard. METHODS: A systematic search was conducted in 9 electronic databases (PubMed/MEDLINE, EBSCOhost, Cochrane Library, Lilacs, SPORTDiscus, CINAHL, Web of Science, AgeLine, and SciELO), gray literature, and in bibliographic and citation searching of selected articles between May and December 2022 and updated between February and March 2024. Two independent reviewers screened citations for inclusion. Data extraction was performed by 1 reviewer and verified by a second reviewer. A frequency of strategies and content analysis syntheses were conducted. RESULTS: A broad search strategy was used, initially identifying 25,945 articles and including 6 randomized clinical trials. The gray literature search identified 18 records. Five studies included forearm fractures, 1 upper limb fracture, and no study exclusively on rehabilitation after humerus fractures. None of the studies provided comprehensive multifactorial fall risk assessments to guide tailored interventions. Assessments mainly focused on gait and balance. Exercise was the most offered intervention alone or in combination with education. Exercise programs were aligned with recommendations to include progressive balance and functional exercises overall. However, the frequency of ≥3 times weekly was less frequently offered. The gray literature showed a lack of fall prevention-specific information after upper limb fractures and mostly called attention to fall prevention after hip fractures. CONCLUSION: Upper limb fracture rehabilitation in older adults, considered at high risk of falling, did not include comprehensive and tailored multifactorial fall assessment and intervention. Unequivocally, exercise programs were overall aligned with recent recommendations and were the most frequent intervention. There is a crucial gap for humerus fractures. This study can help align the treatment of upper limb fractures with updated fall prevention recommendations and impact future research, guiding and influencing implementation in clinical practice. IMPACT: There is an urgent need to implement comprehensive and tailored multifactorial fall assessments and interventions in rehabilitation programs for older adults recovering from upper limb fractures. Guidelines should direct this work to enhance clinical practice.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| 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".