Measuring static and dynamic lower limb alignment in patients with advanced knee osteoarthritis using markerless motion capture
Bibliographic record
Abstract
PURPOSE: Marked lower limb malalignment is associated with the progression of knee osteoarthritis (OA). Markerless motion capture is a computer-vision tool that can measure lower limb alignment throughout both static and dynamic tasks. RESEARCH QUESTIONS: The primary objective of the study was to quantify static and dynamic lower limb alignment for patients diagnosed with advanced medial and lateral knee OA. This analysis also explored sex differences in alignment from the static and dynamic tasks. The secondary objective was to investigate if the mean knee adduction angle during quiet standing and the peak knee adduction angle during the first half of stance of gait were associated. METHODS: Ninety-two patients (37 male, 55 female) diagnosed with advanced knee OA (83 with predominantly medial knee OA, and 9 with predominantly lateral knee OA) completed a quiet standing task and a gait task for markerless motion capture using Theia3D software. Two-way analysis of variance tests were used to investigate sex differences and unpaired t-tests were computed to compare knee OA groups. RESULTS: Statistically significant differences in the knee adduction angle were found between medial and lateral knee OA groups during quiet standing (p < 0.001) and the first half of stance of gait (p < 0.0001). A strong correlation was found (Spearman's ρ = 0.86, p < 0.0001) in lower limb alignment between the static and gait tasks. SIGNIFICANCE: These results show how markerless technology was able to quantify lower limb alignment and demonstrates potential for integration into clinic to assess musculoskeletal diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".