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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.052 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".