To Resurface or Not to Resurface the Patella in Total Knee Arthroplasty
Bibliographic record
Abstract
UNLABELLED: The management of the patellar articular surface at the time of primary total knee arthroplasty (TKA) is controversial. We used expected-value decision analysis to determine whether the patella should be resurfaced in TKA, and also whether secondary resurfacing on an unresurfaced patella is worthwhile. Outcome probabilities and utility values were derived from randomized controlled trials only. A decision tree was constructed and fold-back analysis was performed to ascertain the best treatment path. Sensitivity analyses were performed to determine the effect on decision-making of varying outcome probabilities and utilities. Our model showed patellar resurfacing is the best management strategy for the patella at the time of primary TKA. This decision is robust to changes in the specific data: the best path would remain the same as long as the incidence of persistent anterior knee pain (AKP) with resurfacing remains less than 29% (current mean, 12%) or the incidence of AKP after nonresurfacing falls below 12% (current mean, 26%). Delayed (ie, secondary) patellar resurfacing for ongoing patellar pain provides inferior results for the majority of patients. LEVEL OF EVIDENCE: Level II, decision analysis. See the Guidelines for Authors for a complete description of levels of evidence.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".