Test–retest reliability of the peak knee adduction moment during walking in patients with medial compartment knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To estimate the test-retest reliability of the peak external knee adduction moment during walking in patients with medial compartment knee osteoarthritis (OA), and to describe the interpretation of the reported values. METHODS: A total of 31 patients diagnosed with knee OA confined primarily to the medial compartment underwent quantitative gait analyses during 2 separate test sessions at least 24 hours apart and within 1 week. The peak knee adduction moment was calculated for each patient at each session based on the mean of 5 walking trials. Reliability was estimated using the intraclass correlation coefficient (ICC(2,1)) and the standard error of measurement (SEM). RESULTS: The mean difference in peak adduction moments between test sessions was 0.1% body weight x height (BW x ht; 95% confidence interval [95% CI] -0.1, 0.3). The point estimate for the ICC was 0.86 (95% CI 0.73, 0.96). The point estimate for the SEM was 0.36% BW x ht (95% CI 0.29, 0.48). CONCLUSION: The ICC suggests that the peak knee adduction moment is appropriate for use when distinguishing among patients, for example, in studies of various interventions intended to decrease dynamic load on the knee medial compartment. The SEM illustrates the importance of considering measurement error and incorporating confidence levels when interpreting an individual patient's peak knee adduction moment value.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".