The impact of PCA derived gait kinematic variations on estimated medial knee contact forces in a knee osteoarthritis population
Bibliographic record
Abstract
Osteoarthritis (OA) is a prevalent musculoskeletal condition leading to functional limitations, especially among the elderly. Current treatments focus on pain relief and functional improvement, however there is a lack of approaches which slow disease progression. A promising approach focusses on reducing knee joint loading, as excessive loading contributes to knee OA progression. This study explores kinematic variations in the knee OA population, utilizing principal component analysis (PCA) to examine gait variations (primitives) in both healthy individuals and those with knee osteoarthritis (KOA) and their implications for knee joint loading. The KOA population exhibited 14 modes of variation representing 95% of the cumulative variance, compared to 20 in the healthy population, indicating lower variability with KOA. The relation between identified gait primitives and knee loading parameters, revealed complex relationships. Surprisingly, modes with the largest kinematic variations did not consistently correspond to the highest variations in knee loading parameters revealing degrees of freedom which may have a larger role in determining joint loading. Moreover, potential gait-retraining strategies for KOA, associating specific kinematic combinations with altered knee loading were identified. The results showed a good agreement with previously applied strategies. However, this study highlights the importance of analyzing whole-body kinematics for effective gait retraining, as opposed to focusing on one single joint variation. The study's insights contribute to understanding the intricate interplay between gait pattern variations and knee joint loading changes in healthy and KOA populations, offering practical applications for guiding interventions and estimating loading parameters.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".