Association of Frequent Knee Bending Activity With Focal Knee Lesions Detected With 3T Magnetic Resonance Imaging: Data From the Osteoarthritis Initiative
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association of baseline frequent knee bending activities with the prevalence and progression of cartilage and meniscal abnormalities over 3 years and to assess the effect of frequent knee bending on the different knee compartments with 3T magnetic resonance imaging (MRI). METHODS: We studied 115 subjects without radiographic knee osteoarthritis (OA) but with risk factors for OA from the Osteoarthritis Initiative database. The inclusion criteria at baseline were age 45-55 years, body mass index of 19-27 kg/m(2) , Western Ontario and McMaster Universities Osteoarthritis Index pain score of 0, and Kellgren/Lawrence grade <2. Knee bending activities (kneeling, squatting, stair climbing, and weight lifting) were assessed by questionnaire at the baseline clinic visit. Cartilage and meniscal abnormalities were graded using the Whole-Organ MRI Score. Logistic regression was used to determine the association of frequent knee bending with cartilage and meniscal abnormalities. RESULTS: Frequent knee bending activities were associated with an increased risk of prevalent cartilage lesions (odds ratio [OR] 3.63, 95% confidence interval [95% CI] 1.39-9.52), in particular in the patellofemoral compartment (OR 3.09, 95% CI 1.22-7.79). The increase in risk was higher in subjects involved in ≥2 knee bending activities. At 3-year followup, individuals reporting frequent knee bending were more likely to show progression of cartilage damage (OR 4.12, 95% CI 1.27-13.36) and meniscal abnormalities (OR 4.34, 95% CI 1.16-16.32). CONCLUSION: Frequent knee bending activities were associated with a higher prevalence of knee cartilage lesions (particularly in the patellofemoral compartment) and with an increased risk of progression of cartilage and meniscal lesions in asymptomatic middle-aged subjects.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".