Computer-assisted patellar resection for total knee arthroplasty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Incorrect patellar resection during total knee arthroplasty can lead to anterior knee pain, patellar maltracking, patellofemoral impingement, patellar fracture, component loosening and reduced range of motion. A computer-assisted surgery (CAS) system was developed to improve the accuracy of the patellar cut. Twelve cadaveric knee specimens (6 pairs) were surgically prepared and the patella resected by two senior orthopaedic residents using either a conventional sawguide technique (right knee) or a computer-assisted sawguide technique (left knee). Multiple cuts and measurements were permitted for the conventional technique, to reflect the clinical situation, whereas only a single cut was permitted for the CAS technique. Prior training had been provided on artificial bones for both techniques. Custom marker arrays were mounted on the sawguide and patella. The user positioned the sawguide based on a real-time display that compared the current sawguide plane to the ideal resection. The resulting mediolateral and superoinferior resection angles and central thickness were measured from CT scans of the specimens, relative to the anterior surface of the patella. Both techniques resulted in symmetric cuts (<7°). Repeatability in the mediolateral direction was better for the CAS technique than for the conventional technique (p<0.01). This study demonstrated that computer-assisted patellar resection is a feasible approach that can produce results equal to or better than those obtained with conventional techniques, even when the experimental conditions favor the conventional technique. Improvements in the CAS hardware could further improve the accuracy and usability of the system, resulting in reductions in postoperative complications. Patellar CAS could also serve as a valuable tool for feedback and training.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it