Association of postural control with muscle strength, proprioception, self-reported knee instability and activity limitations in patients with knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To determine the association of postural control with muscle strength, proprioception, self-reported knee instability and activity limitations in patients with knee osteoarthritis. METHODS: A total of 284 patients with knee osteoarthritis from the Amsterdam Osteoarthritis cohort were included. Postural control was assessed using the One-Leg Stand Test (OLST), in which the patients were asked to stand on one leg for 30 s. Muscle strength (isokinetic dynamometer), proprioception (joint motion detection threshold) and self-reported knee instability (episodes of buckling, shifting or giving way) were also assessed. Activity limitations were assessed using the Get Up and Go (GUG) test, the walking up-down stairs test, and Western Ontario and McMaster University Osteoarthritis Index - Physical Function subscale. Regression analyses were used to assess the associations. RESULTS: Muscle weakness (p = 0.02) and proprioceptive inaccuracy (p < 0.001) were associated with decreased postural control. Decreased postural control was associated with less time performing the GUG test (p < 0.001) and the walking up-down stairs test (p < 0.001). These associations were found after adjustment for relevant confounders. CONCLUSION: In patients with knee osteoarthritis, decreased postural control is associated with muscle weakness, proprioceptive inaccuracy and performance-based activity limitations. These results highlight the importance of including assessment and training of postural control in this group of patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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".