Inter-Limb Strength Asymmetry and Risk of Total Knee Replacement
Bibliographic record
Abstract
OBJECTIVE: The current study explored the association between knee extensor strength asymmetry and the risk of total knee arthroplasty in individuals with or at risk of knee osteoarthritis. DESIGN: This longitudinal cohort study analyzed data from the Osteoarthritis Initiative, including 3860 individuals with or at risk for knee osteoarthritis. Participants were categorized as having symmetrical or asymmetrical knee extensor strength based on a 10% difference between limbs. Kaplan-Meier curves and Cox regression assessed the risk of total knee arthroplasty over 10 yrs, adjusting for age, sex, body mass index, baseline Kellgren-Lawrence grade, absolute weakness, and baseline pain. RESULTS: Participants with asymmetrical knee extensor strength had a 30% greater risk of undergoing total knee arthroplasty over 10 yrs compared to those with symmetrical strength (hazard-ratio: 1.30, 95% CI [1.05,1.62]). Limb-specific analyses revealed that a 10% reduction in right and left knee extensor strength were associated with a 40% and 80% increased risk of right and left total knee arthroplasty, respectively. CONCLUSIONS: Knee extensor strength asymmetry was associated with the risk of total knee arthroplasty in individuals with or at risk for knee osteoarthritis. Findings support the need to further examine if an intervention targeted at quadriceps strengthening aimed at achieving and maintaining strength symmetry can reduce total knee arthroplasty risk.
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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 | 0.003 |
| 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".