Quadriceps Weakness and Risk of Knee Cartilage Loss Seen 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 assessed 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) versus 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 Arthritis Index pain score, and baseline MRI cartilage score. RESULTS: Of 163 subjects, 54% were female, with a mean age of 57.7 years. Quadriceps weakness was seen in 11.9% of the subjects. Weakness was a predictor of whole knee cartilage loss (HR 3.48, 95% 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. To our knowledge, 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 | 0.000 |
| 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".