Patellar Resurfacing Versus Patellar Nonreusrfacing in Total Knee Arthroplasty: A Retrospective Study
Bibliographic record
Abstract
Background: Patellar resurfacing in total knee arthroplasty (TKA) is a matter of long-standing debate and there is no consensus regarding the superiority of either patellar resurfacing or patellar nonresurfacing. Objectives: We aimed to compare the outcomes of patellar resurfacing with patellar nonresurfacing in a cohort of knee OA patients sustaining a TKA. Methods: In this retrospective study, patients who had undergone TKA between 2001 and 2011 in two hospitals in Tehran, Iran, were included. The Persian version of Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) was used to quantify the health status of patients. Post-operative complications and rate of reoperation were also compared between the two study groups. Results: The study population consisted of 89 patients in the resurfacing and 72 patients in the nonresurfacing groups. The demographic characteristics of the patients were not significantly different. The mean total WOMAC scores were 19.1 ± 8.8 and 19.6 ± 9.7 for the resurfacing and nonresurfacing groups (P = 0.55). No significant difference was observed between the mean WOMAC subscale scores of the two study groups including pain (P = 0.73), stiffness (P = 0.24), and physical function (P = 0.84). Two reoperations (2.2%) were performed in the resurfacing group and one (1.4%) in the nonresurfacing group. Conclusions: The health status and rate of reoperation were not considerably different between the patellar resurfacing and nonresurfacing groups. These results reveal that patellar resurfacing is not necessary in TKA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".