Guiding Device for the Patellar Cut in Total Knee Arthroplasty: Design and Validation
Bibliographic record
Abstract
An incorrect cut of the patella (kneecap) during total knee arthroplasty, affects the thickness in different quadrants of the patella, leading to pain and poor function. Because of the disadvantages of existing devices, many surgeons choose to perform the cut freehand. Given this mistrust of existing devices, a quick, but accurate, method is needed that guides the cut, without constraining the surgeon. A novel device is described that allows the surgeon to mark a line at the desired cutting plane parallel to the front (anterior) surface using a cautery tool, remove the device, and then align the saw guide, reamer, or freehand saw with the marked line to cut the patella. The device was tested on 36 artificial patellae, custom-molded from two shapes considered easier and harder to resect accurately, and eight paired cadaveric specimens, each in comparison to the conventional saw guide technique. The mediolateral angle, superoinferior angle, difference from intended thickness, and time were comparable or better for the new device. Addressing the remaining outliers should be possible through additional design changes. Use of this guidance device has the potential to improve patellar resection accuracy, as well as provide training to residents and a double-check and feedback tool for expert surgeons.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".