Evaluation of gait cycle time variability in patients with knee osteoarthritis using a triaxial accelerometer
Bibliographic record
Abstract
Abstract Knee osteoarthritis can alter gait variability. However, few studies have compared the temporal factors of the gait cycle between patients with knee osteoarthritis and healthy subjects. Furthermore, no studies have investigated the relationship between gait variability and potential contributing factors (knee joint functions such as muscle strength) in knee osteoarthritis. The first objective of this study was to compare gait cycle variability between female patients with knee osteoarthritis and healthy elderly women to determine gait characteristics in patients with knee osteoarthritis. The second objective was to examine whether gait cycle variability in knee osteoarthritis is associated with potential contributing factors. Twenty-four female patients diagnosed with knee osteoarthritis and 12 healthy elderly women participated. Gait cycle variability (coefficient of variation of gait cycle time), knee extension range of motion, knee extension strength, 5-meter walk test, Timed Up & Go Test, and Western Ontario and McMaster Universities Osteoarthritis Index were measured. All assessment results were compared between the knee osteoarthritis and healthy groups. Gait cycle variability was significantly higher in the knee osteoarthritis group (3.2%±1.5%) compared to the healthy group (2.1%±0.7%). A significant positive correlation was found between the gait cycle variability and 5-meter walk test (r=0.46) and Western Ontario and McMaster Universities Osteoarthritis Index (r=0.43). The gait of patients with knee osteoarthritis may be more unstable than that of healthy individuals. In addition, unstable gait may be associated with gait speed and quality of life. Therefore, we believe that rehabilitation to improve unstable gait can enhance the quality of life of patients with knee osteoarthritis.
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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 | 0.000 |
| 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 | 0.000 | 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".