Gait Analysis after Total Knee Arthroplasty with Posterior Cruciate Ligament Retention Rotating Platform and Fixed Total Knee Arthroplasty
Bibliographic record
Abstract
To investigate the gait analysis of patients after total knee arthroplasty with posterior cruciate ligament retention rotating platform and fixed total knee arthroplasty, so as to provide ideas for clinical rationalization treatment. A total of 82 patients with posterior cruciate ligament preserving rotating and fixed platforms were selected for knee replacement in our hospital and were followed up for 1 y. The knee joint Hospital for special surgery score, Western Ontario and McMaster Universities Osteoarthritis index score and the application of a three-dimensional motion capture system were used to compare the parameters of knee joint kinematics and dynamics. In the application of knee arthroplasty between the posterior cross retained rotating platform and the fixed platform, the temporal and spatial parameters and motion parameters in the knee function and gait analysis were significantly improved compared with those before the operation and the differences were statistically significant (p<0.05), while there was no statistically significant difference between the two groups (p>0.05). Both rotating platform and fixed platform can significantly improve the knee joint function and gait of patients with knee arthroplasty using posterior cruciate ligament retention prosthesis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".