[Comparative study on gait function one year after HURWA robotic-assisted and MAKO robotic-assisted total knee arthroplasty based on MediaPipe motion capture].
Bibliographic record
Abstract
OBJECTIVE: To systematically assess the differences in gait parameters and clinical efficacy between HURWA robot-assisted total knee arthroplasty(TKA) and MAKO robotic-assisted TKA during the 1-year postoperative follow-up period. METHODS: . In the MAKO group, there were also 20 patients, consisting of 4 males and 16 females, with an age range of 58 to 80 years old with an average of (67.50±6.88) years old, BMI ranging from 25.39 to 29.30 kg·m-2 with an average of(26.86 ±1.23) kg·m-2. To comprehensively evaluate the improvement in knee joint function, the Western Ontario and McMaster Universities osteoarthritis index (WOMAC) and American Knee Society score (KSS) were used for clinical efficacy evaluation. In gait analysis, an innovative computer vision-based human pose estimation framework, MediaPipe, was used to quantitatively measure the spatiotemporal parameters (such as walking speed, step frequency, stride length, step width, etc.) and kinematic parameters (such as gait cycle, stance time, stance phase, swing time, swing phase, knee joint active flexion angle, etc.) of both groups preoperatively and 1 year postoperatively. A dynamic evaluation of the maximum hip flexion and knee flexion angles during functional activities (such as squatting) was also conducted to fully reflect the recovery of patients' motor function. RESULTS: >0.05). CONCLUSION: Both HURWA robot-assisted TKA and MAKO robot-assisted TKA demoonstrated equivalent outcones in terms of functional recovery and gait improvement 1 year postoperatively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".