Association Between Knee Load and Pain: Within‐Patient, Between‐Knees, Case–Control Study in Patients With Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: The association between knee loading and pain in patients with knee osteoarthritis is reported to be low and of questionable importance, but may be confounded by several factors that differ between patients. We aimed to elucidate the association between dynamic knee load and pain by minimizing confounding using a study design that was within the same patient, with knees discordant for pain. METHODS: A total of 265 patients with knees discordant for pain (530 knees) rated the pain in each knee before and after walking for 6 minutes, and then underwent 3-dimensional gait analysis. RESULTS: The peak knee adduction moment and knee adduction impulse (proxies for medial knee loading) were associated with increased pain (odds ratio [OR] 2.43 [95% confidence interval (95% CI) 1.77-3.33] and OR 6.62 [95% CI 3.46-12.7], respectively) and remained significant after controlling for radiographic disease severity. When split into quartiles, ORs indicated knees in the highest loading quartile had greater odds of experiencing increased pain with walking (OR 4.7 95% CI 2.3-9.5] for peak adduction moment; OR 9.0 [95% CI 4.0-20.1] for adduction impulse) compared to knees in the lowest loading quartile. CONCLUSION: When between-patient confounding is minimized, there is a strong association between medial knee load and increased knee pain during walking.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".