Patellar Resurfacing Compared with Nonresurfacing in Total Knee Arthroplasty
Bibliographic record
Abstract
Patellar resurfacing in total knee arthroplasty remains controversial. This study compared the long-term clinical outcomes of total knee arthroplasties performed with and without the patella resurfaced and is an update of a previous report. Eighty-six patients (118 knees) underwent primary total knee replacement and were randomized into two groups: those treated with and those treated without resurfacing of the patella. Outcomes included the scores according to the Knee Society clinical rating system, the scores according to a forty-one-question patellofemoral-specific patient questionnaire, patient satisfaction, global and anterior knee pain scores, radiographic findings, and complications and revisions. Fifty-seven patients (seventy-eight knees) were followed for a minimum of ten years. No significant differences were identified between the two groups in terms of the range of motion, Knee Society scores, satisfaction, global knee pain, or anterior knee pain. The overall revision rates in the original series of 118 knees were 12% in the nonresurfacing group and 9% in the resurfacing group. Seven patients (12%) in the nonresurfacing group and two patients (3%) in the resurfacing group underwent revision for a reason related to a patellofemoral problem. On the basis of these findings, we concluded that, with the type of total knee arthroplasty used in our patients, similar results may be achieved with and without patellar resurfacing.
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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 | 0.002 | 0.006 |
| 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) | 0.001 | 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".