Risk Factors for Falls in Older Adults with Lower Extremity Arthritis: A Conceptual Framework of Current Knowledge and Future Directions
Bibliographic record
Abstract
PURPOSE: As the numbers of Canadians aged 65 years and over increases over the next 20 years, the prevalence of chronic conditions, including arthritis, will rise as will the number of falls. Although known fall-risk factors are associated with hip and knee osteoarthritis (OA), minimal research has evaluated fall and fracture risk and/or rates in this population. Thus, the purpose was to summarize research on fall and fracture risk in older adults with hip or knee OA and to develop a conceptual framework of fall-risk screening and assessment. METHOD: The International Classification of Functioning, Disability and Health, clinical practice guidelines for fall-risk screening, and a selected literature review were used. RESULTS: Gaps exist in our knowledge of fall and fracture risk for this population. Muscle performance, balance, and mobility impairments have been identified, but little is known about whether personal and environmental contextual factors impact fall and fracture risk. Physical activity may help to prevent falls, but non-adherence is a problem. CONCLUSION: A need exists to assess fall risk in older adults with hip and knee OA. Promoting regular physical activity by focusing on disease- and activity-specific personal contextual factors may help direct treatment planning.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".