Development of a bone-mounted dynamic physical constraint robot for unicompartmental knee arthroplasty
Bibliographic record
Abstract
Knee osteoarthritis is the most common form of lower-limb osteoarthritis, where cartilage wears away and causes pain. Unicompartmental knee arthroplasty (UKA) and total knee arthroplasty (TKA) are common treatment options. UKA replaces the knee articulation surfaces by prosthesis only at the degenerated tibial-femoral compartment, while TKA replaces the entire knee joint surfaces. UKA could lead to better functional results and faster recovery, but the technique may be under-utilized due to higher risk of revision surgery. In previous work, our group has developed a lower-cost bone-mounted robot for TKA surgeries. In this project, our goal was to adapt this platform for use in UKA procedures. Our new robot design implements a guidance concept called dynamic physical constraint (DPC), which mechanically emulates rigid contact with a virtual fixture, a 3D surface stored in computer space, while allowing smooth motion parallel to that surface. The robot consists of a rotary-prismatic-prismatic joint configuration, followed by a remote center of motion mechanism that holds a hand mill at the end-effector. During an operation, the robot is mounted to the patient’s femur, establishing a robust robot-bone relative position, and the robot imposes accuracy and safety for the surgeon who operates it with both hands. We built a functional robot prototype and performed medial-femoral milling tests on an experimental platform which uses femoral condyle models made of medium density fibreboard as milling targets. Two types of virtual fixture geometries – combined-curved and tri-planar – were tested. Analysis of the laser-scanned post-milling surfaces revealed that the average RMS deviation was 0.33 mm (SD = 0.06 mm) for the combined-curved surface and 0.41 mm (SD = 0.05 mm) for the tri-planar surface. We also conducted inter-specimen surface comparisons and found an average RMS deviation of 0.07 mm (SD = 0.01 mm) for the combine-curved surface and 0.07 mm (SD = 0.02 mm) for the tri-planar surface. In this controlled experimental scenario, our UKA robot successfully achieved the goal of sub-millimetric milling accuracy, and the repeatability of milled surface geometry between different milling attempts seems high. We thus conclude that this robot design should be advanced to the next stage of development.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".