Functional alignment maximises advantages of robotic arm‐assisted total knee arthroplasty with better patient‐reported outcomes compared to mechanical alignment
Bibliographic record
Abstract
PURPOSE: Robotic arm-assisted total knee arthroplasty (RTKA) enables adjustment of implant position to achieve the surgeon's preferred alignment. However, the alignment concept that most effectively enhances patient satisfaction remains unclear. This study compares the clinical outcomes of patients who underwent functionally aligned RTKA (FA-RTKA), mechanically aligned conventional TKA (MA-CTKA) and mechanically aligned RTKA (MA-RTKA). METHODS: A prospectively collected database was retrospectively reviewed for patients who underwent primary TKA for knee osteoarthritis. One hundred and forty-seven knees were performed with MA-CTKA, followed by 72 consecutive knees performed with MA-RTKA, and subsequently, 70 consecutive knees performed with FA-RTKA were enrolled. After 1:1 propensity score matching of patient demographics, 70 knees were finally included in each group. The extent of additional soft tissue release during surgery was identified, and the Coronal Plane Alignment of the Knee classification was utilised to categorise the alignment. At the 1-year follow-up, patient-reported outcomes, including the pain Visual Analogue Scale, Knee Society Score, Western Ontario and McMaster Universities Arthritis Index and Forgotten Joint Score-12, were also compared among the groups. RESULTS: The FA-RTKA group showed significantly less additional soft tissue release than the MA-CTKA and MA-RTKA groups (15.7%, 38.6% and 35.7%, respectively; p = 0.006). Statistically significant differences in functional scores were observed in the postoperative 1-year clinical outcomes in favour of the FA-RTKA group, which had a significantly larger percentage of knees that maintained constitutional alignment and joint line obliquity than those of the other groups. CONCLUSIONS: Functionally aligned TKA showed superior 1-year postoperative patient-reported outcomes compared with those of conventional and robotic arm-assisted mechanically aligned TKA. Therefore, the advantage of RTKA is maximised when the implant positioning is based on functional alignment. The application of RTKA with mechanical alignment has been proven ineffective in improving the clinical outcomes of patients. LEVEL OF EVIDENCE: Level III.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".