Risk Factors for Fear of Falling in Elderly Patients with Severe Knee Osteoarthritis before and One Year after Total Knee Arthroplasty
Bibliographic record
Abstract
PURPOSE: To evaluate the regression of fear of falling (FOF) and identify its risk factors in patients with severe knee osteoarthritis before and one year after total knee arthroplasty (TKA). METHODS: 11 men and 57 women with a mean age of 73 years and a mean body mass index of 30.36 kg/m2 who had severe (grade 3 or 4) knee osteoarthritis and knee pain of ≥1 year were included. Two weeks before and one year after TKA, patients were asked about their FOF status and falls history. Patients were asked to complete the Physical Activity Scale for the Elderly, Short Form 36 (SF-36), and Western Ontario and McMaster Universities Arthritis Index (WOMAC) questionnaires. Clinical performance was assessed using the Berg Balance Scale and Timed Up and Go (TUG) test. RESULTS: Of the 68 patients, 56 (82.4%) had FOF preoperatively and 30 (44.1%) had FOF one year after TKA (p<0.001). The strongest predictors for FOF preoperatively were fallers (odds ratio [OR]=9.83, p=0.028), mental component summary (MCS) score of SF-36 (OR=0.88, p=0.024), and TUG (OR=3.4, p=0.013). The strongest predictors for FOF one year postoperatively were fallers (OR=16.51, p=0.041), patients with ≥2 chronic diseases (OR=17.33, p=0.011), physical function score of WOMAC (OR=1.015, p=0.005), and MCS score of SF-36 (OR=0.86, p=0.015). CONCLUSION: TKA positively affected FOF and gradually reduced the FOF rate over a year period after TKA in an elderly population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".