A Single Surgeon Experience of Selective Patellar Resurfacing During Primary Total Knee Arthroplasty
Bibliographic record
Abstract
Background Routine patellar resurfacing remains controversial in primary total knee arthroplasty (TKA). This study reports the experience of a high-volume arthroplasty surgeon who stopped routinely resurfacing patellae for a 3-year period. Methods All primary TKAs performed by a single surgeon between January 2018 and September 2022 with minimum 1-year follow-up were retrospectively reviewed. Data were analyzed between cohorts—nonresurfaced and resurfaced patellae—and between phases—universal and selective resurfacing. Outcomes included reoperation, patellar complications, and patient-related outcome measure scores. Results Five hundred four primary TKAs, with mean 24-month follow-up, were included. Patellar resurfacing was performed in 77% of the overall cohort, including 58% in the selective and 100% in the universal phases. Reoperation (7.6% vs 0.3%; P < .001) and patellar complications (8.4% vs 1.3%; P < .001) were higher in the nonresurfaced vs resurfaced cohort. Eight of the 9 reoperations in the nonresurfaced group were for secondary resurfacing, and all were female ( P = .017). Mean 12-Item Short Form Health Survey Physical Health ( P = .037) and Western Ontario and McMaster Universities Arthritis Index Pain scores ( P = .002) were better in the resurfaced cohort. Selective resurfacing demonstrated a higher reoperation rate (3.3% vs 0.4%; P = .022) and worse Western Ontario and McMaster Universities Arthritis Index Pain ( P = .026) and Knee Society Knee Functional scores ( P = .042). Conclusions Cessation of routine patellar resurfacing led to inferior clinical results and an unacceptably high early reoperation rate, specifically among women. The generalizability of these findings may be limited due to surgeon-specific factors; however, we urge caution in surgeons who consider similar changes in practice. Level of Evidence Level III.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".