Quantifying Achievable Levels of Improvement in Knee Joint Biomechanics During Gait After Total Knee Arthroplasty Relative to Osteoarthritis Severity
Bibliographic record
Abstract
Total knee arthroplasty (TKA) surgery improves knee joint kinematics and kinetics during gait for most patients, but a lack of evidence exists for the level and incidence of improvement that is achieved. The objective of this study was to quantify patient-specific improvements in knee biomechanics relative to osteoarthritis (OA) severity levels. Seventy-two patients underwent 3-dimensional (3D) gait analysis before and 1 year after TKA surgery, as well as 72 asymptomatic adults and 72 with moderate knee OA. A combination of principal component analysis and discriminant analyses were used to categorize knee joint biomechanics for patients before and after surgery relative to asymptomatic, moderate, and severe OA. Post-TKA, 63% were categorized with knee biomechanics consistent with moderate OA, 29% with severe OA, and 8% asymptomatic. The magnitude and pattern of the knee adduction moment and angle (frontal plane features) were the most significant contributors in discriminating between pre-TKA and post-TKA knee biomechanics. Standard of care TKA improves knee biomechanics during gait to levels most consistent with moderate knee OA and predominately targets frontal plane features. These results provide evidence for the level of improvement in knee biomechanics that can be expected following surgery and highlight the biomechanics most targeted by surgery.
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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 | 0.001 | 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".