Effects of Denervation on the Mid-term Results of Knee Joint Functions after Non-resurfaced Total Knee Arthroplasty
Bibliographic record
Abstract
Objective: Total knee arthroplasty (TKA) has become an effective treatment modality for end-stage osteoarthritis and rheumatoid arthritis. Postoperative problems such as anterior knee pain affect postoperative patient satisfaction. Patellar denervation has been proposed as a technique to relieve pain, but its efficacy remains controversial. This study evaluated the mid- and long-term effects of patellar denervation on postoperative knee joint functions, hoping to provide better guidance for clinical practice. Methods: This study is a prospective randomized controlled double-blind study. 58 patients undergoing bilateral non-resurfaced total knee arthroplasty were included and randomized into two groups. Both groups underwent total knee arthroplasty while patellar denervation was performed only on the experimental group. Information regarding whether if patellar denervation was performed were withheld from all patients and outcome assessors. All surgeries were performed by the same high-level professional physician, and the post-stable knee prosthesis system (PS Scorpio NRG PS, Stryker) was used during the surgeries. The knee joint functions were evaluated by professional assessors before and after surgery. The evaluation indicators mainly include KSS scoring, Western Ontario and McMaster Universities (WOMAC) scoring and Visual Analogue Scale (VAS), FJS scoring, etc. The follow-up period was 3 years and 5 years after surgery. Results: The experimental group had better KSS and FJS scores than the control group, the difference was statistically significant. There was no significant inter-group difference in WOMAC and VAS scores. Conclusion: The patellar denervation in TKA patients has positive effects on the mid- and long-term recovery of knee joint functions, and the postoperative satisfaction is better.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".