The Effect Of Foot Progression Angle On Knee Joint Loads During Gait
Bibliographic record
Abstract
The mechanical loads placed on the knee during gait, in particular the adduction moment (KAM), have been implicated in the progression of knee osteoarthritis (OA). Those presenting with symptomatic medial compartment knee OA tend to adopt a toe out gait pattern reducing the peak KAM during gait, hence unloading the diseased medial compartment. Although this is known, the relationship between the foot progression angle (FPA) and the KAM, along with other knee gait kinetic measures has yet to be determined. PURPOSE To determine the relationship between FPA and 3D knee gait forces and moments. METHODS Gait analysis was performed on 11 healthy subjects (23 +/− 1.7 yrs). Subjects performed five trials in each of three conditions: natural foot position (NFP), approximately 30 degrees internally rotated (IR) and approximately 30 degrees externally rotated (ER) from their natural position. 3D knee joint forces and moments were compared to true FPA (ANOVA) and a regression analysis determined the relationships between normalized (by NFP) values. RESULTS Late stance peak KAM and medial-lateral shear forces (MLF) were significantly different across conditions, (KAM: ER=0.125, NFP=0.267, IR=0.410 Nm/kg; p<0.01 and MLF: ER=−0.980, NFP=−1.248, IR=−1.447 N/kg; p<0.01). See Figure 1 for average gait curves. There were also strong relationships between changes in FPA (actual range of 49.3 ±) and changes in KAM (R2=.97) and MLF (R2=.88).FigureCONCLUSIONS Results suggest that for every 10 degree change in FPA there is a 21.7% change in KAM and a 7.6% change in MLF during late stance. This may have implications in preventing the development 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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".