Knee biomechanics variability before and after total knee arthroplasty: an equality of variance prospective study
Bibliographic record
Abstract
This study evaluated gait variability in patients before and after total knee arthroplasty (TKA) using the equality of variance method to determine where variability differences occur in the movement cycle. Twenty-eight patients underwent TKA with cruciate-sacrificed implants. Patients underwent motion analysis which measured knee biomechanics as they walked overground at their preferred pace before and 12 months after TKA. Equality of variance results were compared with 14 healthy controls of similar age. Before surgery, patients had reduced knee extension moment variability throughout the early stance phase (4-21% gait cycle, p < 0.05) compared to controls. Knee power variability was lower preoperatively compared to controls for most of the stance phase (0-13% and 17-60% gait cycle, p < 0.05). Sagittal knee moment and power variability further decreased following TKA. Knee extension moment variability was lower postoperatively throughout stance phase compared to preoperatively (4-22% and 36-60% gait cycle, p < 0.05) and compared to controls (4-30% and 45-60% gait cycle, p < 0.05). Knee power variability remained lower following TKA throughout stance phase compared to preoperatively (10-24% and 36-58% gait cycle, p < 0.05) and controls (3-60% gait cycle, p < 0.05). TKA patients may be less stable, and this may be in part due to an unresolved adaptation developed while awaiting TKA surgery and the cruciate sacrificing design of the implants utilized in this study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.001 |
| 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".