Quadriceps Weakness and the Risk of Knee Cartilage Loss on Magnetic Resonance Imaging in a Population-Based Cohort with Knee Pain
Bibliographic record
Abstract
Objective. To determine whether baseline quadriceps weakness predicts cartilage loss on magnetic resonance imaging (MRI). Methods. Subjects aged 40-79 with knee pain (n=163) were recruited from a random population sample and examined for quadriceps weakness with manual isometric strength testing, using a 3-point scoring system (0=poor resistance, 1=moderate resistance, 2=full resistance), which was dichotomized as normal (grade 2) vs weak (grade 0/1). MRI of the more symptomatic knee was obtained at baseline and at mean of 3.3-years. Cartilage was graded 0-4 on MRI. Exponential regression analysis was used to evaluate whether quadriceps weakness was associated with whole knee cartilage loss and, in secondary analyses with compartment specific cartilage loss, adjusted for age, sex, body mass index, Western Ontario and McMaster Universities Osteoarthritis (WOMAC) pain and baseline MRI cartilage score. Results. Of 163 subjects, 54% were female, mean age 57.7 years. Quadriceps weakness was seen in 11.9% of the subjects. Weakness was a predictor of whole knee cartilage loss (hazard ratio [HR] 3.48, 95% confidence interval [CI] 1.30-9.35). Quadriceps weakness was associated with cartilage loss in the medial tibiofemoral (TF) compartment (HR 4.60, 95% CI 1.25-17.02), while no significant association was found with lateral TF (HR 1.53, 95% CI 0.24-9.78) or patellofemoral compartment (HR 2.76, 95% CI 0.46-16.44). Conclusion. In this symptomatic, population-based cohort, quadriceps weakness predicted whole knee and medial TF cartilage loss after 3 years. This is the first study to show that a simple clinical examination of quadriceps strength can predict the risk of knee cartilage loss.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".