Knee Power Is an Important Parameter in Understanding Medial Knee Joint Load in Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To determine the extent to which knee extensor strength and power explain variance in knee adduction moment (KAM) peak and impulse in clinical knee osteoarthritis (OA). METHODS: Fifty-three adults (mean ± SD age 61.6 ± 6.3 years, 11 men) with clinical knee OA participated. The KAM waveform was calculated from motion and force data and ensemble averaged from 5 walking trials. The KAM peak was normalized to body mass (Nm/kg). The mean KAM impulse reflected the mean total medial knee load during stride (Nm × seconds). For strength, the maximum knee extensor moment attained from maximal voluntary isometric contractions (MVIC) was normalized to body mass (Nm/kg). For power, the maximum knee extensor power during isotonic contractions, with the resistance set at 25% of MVIC, was normalized to body mass (W/kg). Covariates included age, sex, knee pain on the Knee Injury and Osteoarthritis Outcome Score, gait speed, and body mass index (BMI). Relationships of the KAM peak and impulse with strength and power were examined using sequential stepwise forward linear regressions. RESULTS: Covariates did not explain variance in the KAM peak. While extensor strength did not, peak knee extensor power explained 8% of the variance in the KAM peak (P = 0.02). Sex and BMI explained 24% of the variance in the KAM impulse (P < 0.05). Sex, BMI, and knee extensor power explained 31% of the variance in the KAM impulse (P = 0.02), with power contributing 7% (P < 0.05). CONCLUSION: Knee extensor power was more important than isometric knee strength in understanding medial knee loads during gait.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".