Better accuracy and reproducibility of a new robotically‐assisted system for total knee arthroplasty compared to conventional instrumentation: a cadaveric study
Bibliographic record
Abstract
PURPOSE: Robotically-assisted total knee arthroplasty (TKA) has been shown to improve alignment and decrease outliers, an important goal in TKA procedures. The purpose of this cadaveric study was to compare the accuracy and reproducibility of a recently introduced TKA robotic system to conventional instrumentation for bone resections. METHODS: This cadaveric study compared 14 robotically-assisted TKA with 20 conventional TKAs. Four board-certified high volume arthroplasty surgeons with no prior experience in robotics (except one) performed the procedures with three different implant systems. Angle and level of bone resections obtained from optical navigation or calliper measurements were compared to the intra-operative plan to determine accuracy. Group comparison was performed using Student t test (mean) and F test (variance), with significance at p < 0.05. RESULTS: The robotic group demonstrated statistically more accurate results (p < 0.05) and fewer outliers (p < 0.05) than conventional instrumentation when aiming for neutral alignment. Final limb alignment (HKA) had an accuracy of 0.8° ± 0.6° vs 2.0° ± 1.6°, with 100% vs 75% of cases within 3° and 93% vs 60% within 2°. For the robotically-assisted knees, the accuracy of bone resection angles was below 0.6° with standard deviations below 0.4°, except for the femur flexion (1.3° ± 1.0°), and below 0.7 mm with standard deviations below 0.7 mm for bone resection levels. CONCLUSION: This in vitro study has demonstrated that this novel TKA robotic system produces more accurate and more reproducible bone resections than conventional instrumentation. It supports the clinical use of this new robotic system. LEVEL OF EVIDENCE: Cadaveric study, Level V.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".