Effect of Patellar Denervation on Anterior Knee Pain and Knee Function in Total Knee Arthroplasty without Patellar Resurfacing: A Meta‐Analysis of Randomized Controlled Trials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: The aim of the present study was to evaluate the effect of patellar denervation (PD) in preventing anterior knee pain (AKP) and improving knee function after total knee arthroplasty (TKA) without patellar resurfacing, and to help surgeons decide whether or not to use PD in TKA. METHODS: The electronic databases of Pubmed, Embase, Cochrane, Web of Science, and Scopus were searched for all randomized controlled trials (RCT) comparing the outcomes of PD and no patellar denervation (NPD) in TKA without patellar resurfacing. Primary outcomes were incidence of AKP, visual analogue scale for pain (VAS), and patellar score (PS). Secondary outcomes were Knee Society Score (KSS), the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), the Oxford Knee Score (OKS), knee range of motion (ROM), and complications. RESULTS: A total of nine RCT met the inclusion criteria. On meta-analysis, PD significantly reduced the incidence of AKP (odds ratio 0.49; 95% confidence interval [CI] 0.26 to 0.92), reduced the VAS (weighted mean difference [WMD] -0.57; 95% CI -1.02 to -0.11), and improved the WOMAC (WMD -4.63; 95% CI -6.49 to -2.77) and the ROM (WMD 9.60; 95% CI 0.39 to 18.81) during the follow-up within 12 months. In addition, PD improved the PS (WMD 1.01; 95% CI 0.65 to 1.38), KSS (WMD 1.12; 95% CI 0.10 to 2.14), and the WOMAC (WMD -1.41; 95% CI -2.74 to -0.08) during the follow-up after 12 months. CONCLUSION: Patellar denervation could significantly reduce the VAS and the incidence of AKP in the early stages after TKA as well as improve the clinical outcomes in terms of the PS, the WOMAC, the KSS, and the ROM. This study demonstrates that PD is a safe and recommendable technique that could be routinely performed in TKA.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.058 | 0.020 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it