Clinically Assessed Mediolateral Knee Motion
Bibliographic record
Abstract
OBJECTIVE: Mediolateral knee movement can be assessed visually with clinical tests. A knee-medial-to-foot position is associated with an increased risk of knee injuries and pathologies. However, the implications of such findings on daily tasks are not well understood. The aim of this study was to investigate if a knee-medial-to-foot position assessed during a clinical test was associated with altered hip and knee joint kinematics and knee joint kinetics during gait compared with those with a knee-over-foot position. DESIGN: Participants were visually assessed during a single-limb mini squat test and classified by a physiotherapist as exhibiting either a knee-medial-to-foot or knee-over-foot position. A comparison of 3-dimensional hip and knee gait kinematics and kinetics between the knee-over-foot and knee-medial-to-foot classifications was performed. SETTING: Research laboratory. PARTICIPANTS: Twenty-five healthy participants were recruited and visually assessed as either knee-over-foot (n = 15; 26.2 ± 6.1 years) or knee-medial-to-foot (n = 10; 24.8 ± 4.1 years). MAIN OUTCOME MEASURES: Peak knee valgus angle and peak internal hip rotation during normal gait. RESULTS: No differences were observed in peak knee valgus angle [3.6 (3.7) vs 5.2 (2.5) degrees; P = 0.19], peak internal hip rotation [8.4 (7.0) vs 4.3 (8.1) degrees; P = 0.21], or knee joint kinetics between groups. CONCLUSIONS: A knee-medial-to-foot position observed during the single-limb mini squat was not reflected during gait measured by 3-dimensional motion analysis in knee healthy individuals. Furthermore, those assessed to have a knee-medial-to-foot position did not display increased loading of the knee joint compared with the knee-over-foot group. Care should be taken when extrapolating results from one movement to another.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".