Predictors of single‐leg standing balance in individuals with medial knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To identify factors related to single-leg standing balance in individuals with medial compartment knee osteoarthritis (OA). METHODS: This cross-sectional study assessed clinical, demographic, and biomechanical measures in 57 individuals and their relationships with single-leg standing balance. Differences in age, mass, symptoms, knee pain, radiographic severity, lower extremity alignment, and hip and knee extension as well as hip abduction torques were compared between those who could and could not perform 3 trials of single-leg standing balance. Multiple regression was used to identify predictors of center of pressure (COP) path length in those who could complete the task. RESULTS: Thirty-four individuals (60%) successfully completed all 3 single-leg standing balance trials and were significantly younger (P = 0.003) than those who could not. No other variable was significantly different between the groups. Disease severity, number of painful knees, lower extremity alignment, pain intensity, and quadriceps torque were all significant predictors of COP path length. Specifically, better single-leg standing balance (smaller COP path length) was related to more severe radiographic changes and stronger quadriceps, those with bilateral symptoms, and to less varus malalignment and knee pain. CONCLUSION: Single-leg standing balance in those with medial knee OA is related to the modifiable factors lower extremity alignment, knee pain, and quadriceps strength. Given the reduced balancing ability in this patient population, interventions targeting these factors are necessary.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".