Similar postoperative patient-reported outcome in both second generation patellofemoral arthroplasty and total knee arthroplasty for treatment of isolated patellofemoral osteoarthritis: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Due to inconsistent results and high failure rates, total knee arthroplasty (TKA) is more often used to treat isolated patellofemoral osteoarthritis (PFOA) despite the theoretical advantage of patellofemoral arthroplasty (PFA). It is perceived that second-generation PFA may have improved the outcomes of surgery. In this systematic review, the primary aim was to compare outcomes of second-generation PFA and TKA by assessment of patient-reported outcome measures (PROMs). METHODS: A systematic search was made in PubMed, Medline, Embase, Cinahl, Web of Science, Cochrane Library and MeSH to identify studies using second-generation PFA implants or TKA for treatment of PFOA. Only studies using The American Knee Society (AKSS), The Oxford Knee Score (OKS) or The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) to report on PROMs were included. RESULTS: The postoperative weighted mean AKSS knee scores were 88.6 in the second-generation PFA group and 91.8 in the TKA group. The postoperative weighted mean AKSS function score was 79.5 in the second-generation PFA group and 86.4 in the TKA group. There was no significant difference in the mean AKSS knee or function scores between the second-generation PFA group and the TKA group. The postoperative weighted mean OKS score was 36.7 and the postoperative weighted mean WOMAC score was 24.4. The revision rate was higher in the second-generation PFA group (113 revisions [8.4%]) than in the TKA group (3 revisions [1.3%]). Progression of OA was most commonly noted as the reason for revision of PFA, and it was noted in 60 cases [53.1%]; this was followed by pain in 33 cases [29.2%]. CONCLUSION: Excellent postoperative weighted mean AKSS knee scores were found in both the second-generation PFA group and in the TKA group, suggesting that both surgical options can result in a satisfying patient-reported outcome. Higher revision rates in the second-generation PFA studies may in part be due to challenges related to patient selection. Based on evaluation of PROMs, the use of second-generation PFA seems to be an equal option to TKA for treatment of isolated PFOA in appropriately selected patients. Hopefully, this can be considered by physicians in their daily clinical work. LEVEL OF EVIDENCE: IV.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".