Postoperative knee pain, satisfaction, and revision after total knee arthroplasty with and without primary patella resurfacing
Bibliographic record
Abstract
BACKGROUND: Patella resurfacing during total knee arthroplasty (TKA) remains controversial. We performed a large cohort study to assess pain, function, and revision risk between patients with and without primary patella resurfacing. METHOD: We identified 10,846 primary TKAs in 2005-2024. We ascertained patella-related pain and function from Oxford knee score questions and patient satisfaction at year one and two. We used logistic regression to estimate the association between patella status and these outcomes. We identified revision TKA and used Cox proportional hazard models to estimate its association with patella resurfacing. RESULTS: Around 78 % of patients had primary patella resurfacing during their TKA, 96 % of resurfaced patellas were done with a posterior-stabilized knee system versus 48 % of unresurfaced patellas. We observed no difference in patella-related pain and function by patella status. Patella-related pain had an adjusted odds ratio [OR] of 0.94 (95 % confidence interval [CI] 0.79-1.12) for resurfaced patellas at year one, the OR for patella-related functional issues was 0.98 (95 % CI 0.78-1.22). The hazard ratio for five years of follow up for revision was 0.75 (95 % CI 0.50-1.12). CONCLUSION: Patients with and without primary patella resurfacing had similar patella-related pain and functional outcomes in the first two postoperative years. Unresurfaced patellas had higher revision rates, although this was not statistically significant. A small subset of patients with unresurfaced patellas may have unfavorable outcomes, but these patients may also have been revised in an attempt to reduce anterior knee pain that would have been present with primary patella resurfacing.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".