Association of pain with frequency and magnitude of knee loading in knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Although the relationship between pain and the magnitude of medial knee loading has been previously studied, the contribution of frequency of loading has not. The objective of this study was to determine whether the addition of loading frequency (steps/day) to loading magnitude (knee adduction moment [KAM] impulse) helps explain variance in knee pain in people with knee osteoarthritis (OA). METHODS: Participants were adults with symptomatic knee OA with radiographic signs in the medial knee compartment (n = 38, 10 women). Pain was measured using the pain subscale of the Knee Injury and Osteoarthritis Outcome Score. Participants wore an accelerometer for 1 week to determine the average number of steps/day. The external KAM impulse was calculated from 3-dimensional gait analysis as participants ambulated at self-selected speeds. Knee extensor strength was measured with an isokinetic dynamometer. Linear regression was used to examine the relationship between pain and steps/day after controlling for the KAM impulse, knee extensor strength, and body mass index (BMI). RESULTS: After controlling for BMI (R(2) = 0.02), knee extensor strength (R2 change = 0.26, P < 0.05), and KAM impulse (R2 change = 0.11, P < 0.05), steps/day contributed an additional 9% of variance in pain (P < 0.05). This model accounted for a total of 49% of the variance in pain (F[4,33] = 7.77, P < 0.05). CONCLUSION: Increased knee loading frequency and magnitude were associated with increased pain. Objective measures of loading frequency should be considered when investigating the incidence and progression of knee OA.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".