Functional alignment with robotic‑arm assisted total knee arthroplasty demonstrated better patient‐reported outcomes than mechanical alignment with manual total knee arthroplasty
Bibliographic record
Abstract
PURPOSE: Given the improved accuracy of robot-assisted surgery, robotic-arm assisted functionally aligned total knee arthroplasty (RFA-TKA) aims to preserve the native pre-arthritic knee biomechanics, to achieve balanced flexion-extension gaps. The purpose of this study was to compare the accuracy of the implant position and short-term clinical outcomes of patients who underwent RFA-TKA vs. mechanically aligned total knee arthroplasty with manual technique (MA-TKA). METHODS: A prospectively collected database was reviewed retrospectively for patients who underwent primary TKA. Sixty patients who underwent RFA-TKA between February 2020 and July 2020 were included in the RFA-TKA group. Sixty patients who underwent MA-TKA were included via 1:1 matching for age, sex, and body mass index based on the RFA-TKA group. For radiological evaluation, knee X-rays were used to assess the functional knee phenotype and implant position accuracy by measuring the coronal and sagittal alignment, and these measurements were compared between the two groups. Patient demographic characteristics and patient-reported outcomes including Knee Society scores, Western Ontario and McMaster Universities Arthritis Index, and forgotten joint score-12 were compared between the groups. RESULTS: Statistically significant differences were observed in postoperative 2-year clinical outcomes in favor of RFA-TKA group which showed greater accuracy in the tibial component sagittal alignment than MA-TKA (1.0 ± 2.3 vs. 0.7 ± 1.6, respectively; P < 0.001). However, outliers in the component positions were more common in the MA-TKA group, which was statistically significant for the femoral coronal and tibial sagittal alignments (P = 0.017 and 0.015, respectively). CONCLUSIONS: Functional alignment in TKA could be accurately obtained with the assistance of a robotic arm, and the results showed greater 2 year postoperative patient-reported outcome and satisfaction than mechanically aligned TKA using manual instruments. LEVEL OF EVIDENCE: 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 | 0.002 |
| 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".