Prevalence and Risk Factors of Falls in Adults 1 Year After Total Hip Arthroplasty for Osteoarthritis
Bibliographic record
Abstract
Total hip arthroplasty is very successful in alleviating the pain from osteoarthritis. However, deficits in lower limb strength, gait, and balance after surgery has identified this group at risk of falls. Considering the high number of people annually receiving a total hip arthroplasty, further elaboration of factors associated with falls is needed to refine fall prevention guidelines. The objective was to examine the prevalence and circumstances of falling and the risk factors associated with falling in older adults in the first year after total hip arthroplasty surgery. This was a cross-sectional study involving 108 individuals (age of 72.4 ± 6.5 yrs, 60% females) who had unilateral total hip arthroplasty. The primary outcome was falls and their circumstances during the 12 mos after the total hip arthroplasty. Twenty-five people (23.1%) had at least one fall and most falls (56%) occurred 6-12 mos after surgery. Falls resulted in minor injuries for 44% and 12% reported major injuries. The strongest independent predictor for falls was a history of a previous joint replacement with odds ratio of 7.38 (95% CI = 2.41-22.62, P < 0.001). Overall, the information highlights that falls are common after total hip arthroplasty, yet considering the older age of people having this surgery screening for falls risk should follow established guidelines.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".