Computer‐assisted patellar resection system: Development and insights
Bibliographic record
Abstract
Incorrect resection of the patella during total knee arthroplasty (TKA) can lead to anterior knee pain (AKP), patellar maltracking, patellofemoral impingement, patellar fracture, component loosening, and reduced range of motion. Computer-assisted surgery (CAS) systems for the tibia and femur improve cut accuracy, but no CAS system is available for patellar resection. We developed a system that included an optoelectronic localizer, marker arrays on the patella and instruments, and navigation software. Three users performed resections on artificial patellae mounted in a simulated surgical setup using five techniques (two CAS, three conventional), each repeated at least three times in randomized order. Computer-assisted patellar resection produced better or equal cut symmetry compared to conventional techniques, particularly superoinferiorly. Using CAS with a sawguide produced better results than using CAS freehand with an oscillating saw. This study showed the feasibility of computer-assisted patellar resection, which could lead to reduced pain and complications after TKA. The feedback provided could also make patellar CAS a valuable training tool.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".