Personalized alignment™ for total knee arthroplasty using the ROSA® Knee and Persona® knee systems: Surgical technique
Bibliographic record
Abstract
Total knee arthroplasty (TKA) procedures are expected to increase up to 565% in the United States over the next 3 decades. TKAs were traditionally performed with neutral mechanical alignments that provided equal medial and lateral gaps in extension and flexion to reduce implant wear but were less successful at restoring native knee function and associated with high patient dissatisfaction. Kinematic alignment (KA) restores native anatomy and minimizes soft tissue release; however, KAs that recreate severe deformities and/or biomechanically inferior alignments result in significant increases in implant stress and risk of aseptic loosening. Restricted kinematic alignment (rKA) recreates pre-arthritic anatomy within a range of acceptable alignment boundaries, and improved patient clinical scores and faster recoveries have been reported with rKA techniques. Personalized Alignment™ is an evolution of rKA that relies heavily upon robotic assistance to reliably recreate patient anatomy, native soft tissue laxity, and accurate component placement to improve patients' clinical outcomes. The purpose of this surgical technique report is to describe the Personalized Alignment TKA method using the ROSA ® Knee System and Persona ® The Personalized Knee ® implants. Herein we provide specific procedures for pre-operative planning, anatomical landmarking and evaluation, intra-operative planning and adjustment of resections and cuts, cut validation and soft tissue evaluation with robotic-assisted personalized TKA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 0.002 |
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".