Prediction of knee joint moment changes during walking in response to wedged insole interventions
Bibliographic record
Abstract
Wedged insoles are prescribed for medial knee osteoarthritis to reduce the knee adduction moment; however, it is currently not possible to predict which patients will in fact experience reduced moments. The purpose of this study was to identify a simple method using two-dimensional data for predicting the expected change in knee adduction moments with wedged insoles. Knee adduction moments during walking were determined for healthy individuals (n = 15) and individuals with medial knee osteoarthritis (n = 19) while wearing their own shoe without an insole (control), with a 6-mm medial wedge and with a 6-mm lateral wedge. The percent changes relative to control were determined. Then, participants completed single-step trials with each footwear condition where only the changes in mediolateral positions of the knee joint center, shank center of mass, ankle joint center, and foot center of mass relative to control were determined. These variables were used as predictors in regression equations where the change in knee adduction moment during walking was the dependent variable. The change in mediolateral positions of the lower extremity during a single step significantly predicted the change in knee adduction moment during walking for the lateral wedge in both the healthy (R(2) = 0.72, p = 0.008) and knee osteoarthritis (R(2) = 0.52, p = 0.026) groups, and also for the medial wedge in both the healthy (R(2) = 0.67, p = 0.016) and knee osteoarthritis (R(2) = 0.54, p = 0.020) groups. The method of using mediolateral position data from a single-step movement to predict walking biomechanics was successful. These data are relatively simple to collect and analyze, offering the possibility for future incorporation into a wedge prediction system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".