Individualized Functional Knee Alignment in Total Knee Arthroplasty: A Robotic-assisted Technique
Bibliographic record
Abstract
Introduction: The use of robotic technology is becoming a well-recognized alternative to conventional total knee arthroplasty (TKA). The quantitative soft tissue information generated in robotic surgery can be used to balance the knee and achieve functional alignment (FA) of the components. This paper describes a novel FA technique using an individualized preoperative plan that is then adjusted to achieve soft tissue balance. Materials and Methods: We report on surgical technique, indications, considerations, and complications after our experience of performing 650 functionally aligned TKAs. We collected 2-year patient reported outcomes on 165 TKAs in this series (165 of 193 TKAs have reached 2 years follow-up in the series of 650 TKAs; 85% follow-up rate). Results: We found significant postoperative improvements with few infections and no revisions for mechanical reasons 2 years after surgery with this technique. Patients had improved knee range-of-motion (105 degrees° flexion preoperatively vs. 125 degrees flexion postoperatively; P<0.001), higher Forgotten Joint Scores (17 preoperatively vs. 77 postoperatively; P<0.001), improved Oxford Knee Scores (22 preoperatively vs. 43 postoperatively; P<0.001), higher KOOS Jr scores (48 preoperatively vs. 88 postoperatively; P<0.001) and lower visual analogue score pain scores (70 preoperatively vs. 12 postoperatively; P<0.001) 2 years postoperatively. Discussion: The described surgical technique is a promising method for conducting a robotic TKA. Benefits of FA include improved efficiency with preresection balancing, reduced soft tissue releases compared with a mechanical alignment technique, and accurate bony cuts with robotic assistance. Further studies are required to compare this technique with established methods to determine any differences in outcomes.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".