Risk Factors for Falls in People With a Lower Limb Amputation: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To review the evidence connecting risk factors to falls in adults with a lower limb amputation (LLA) across the continuum of care settings. DESIGN: Systematic review. LITERATURE SURVEY: Electronic database searches were conducted in MEDLINE, Pubmed, CINAHL, and EMBASE covering January 1988 to January 2016. Noninterventional studies, including cohort and cross-sectional studies, were included. Two reviewers independently completed data extraction and quality evaluation. METHODOLOGY: Twelve studies met the inclusion criteria and quality of reporting was evaluated using the criteria by Tooth et al. SYNTHESIS: The average quality of reporting score was 19.8, scores ranged from 16 to 29. Studies covered the acute hospital stay after the amputation, inpatient rehabilitation, and community living. Falls were a common occurrence, with the cohort studies reporting 20.8% for acute hospital stay to 58% in the community years after the amputation. Injurious falls also were common, with an occurrence ranging from 40% to 60%. Risk factors that increase falls and are shared with the general population of older adults include lower extremity muscle weakness, increasing age, comorbidities, and number of prescription medications. Risk factors for falls that are unique to adults with LLA are dysvascular etiology of the amputation, transtibial level of amputation in the postoperative period and transfemoral level postrehabilitation, and reduced sense of vibration. CONCLUSIONS: Falls in adults with an LLA are common from the time of the amputation to years later living in the community. Risk factors vary across care settings after the amputation, which has implications for safety and fall-prevention strategies. LEVEL OF EVIDENCE: III.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".