Robotic-assisted total knee arthroplasty leads to early functional improvement in patient-reported outcome among obese patients: A retrospective cross-sectional study
Bibliographic record
Abstract
Robotic-assisted total knee arthroplasty (RaTKA) improves radiological alignment compared to conventional TKA, but its impact on functional outcomes in obese patients remains unclear. This study evaluates whether RaTKA enhances postoperative functional recovery in obese patients. A retrospective review (January 2009 to May 2024) included consecutive obese patients who underwent unilateral primary TKA by a single senior surgeon using either RaTKA or conventional methods. The body mass index was recorded during the final preoperative clinic visit. Functional outcomes were assessed using the Knee Injury and Osteoarthritis Outcome Score for Joint Replacement (KOOS JR) and the Western Ontario and McMaster Universities Arthritis Index (WOMAC) at three months and one year. Statistical analysis was performed using SPSS version 29. The study included 200 patients in the three-month cohort and 206 patients in the one-year cohort. RaTKA patients were younger and had a lower Charnley Class C rate. At three months, the RaTKA group demonstrated superior functional outcomes, including higher KOOS ADL (82.84 vs. 75.35, p = 0.003), KOOS Sport (58.90 vs. 50.42, p = 0.050), KOOS JR (73.15 vs. 67.54, p = 0.010), WOMAC function (11.73 vs. 16.83, p = 0.002), and WOMAC total (17.93 vs. 24.04, p = 0.009). By one year, outcomes were comparable between groups. RaTKA improves early functional recovery in obese patients but does not confer long-term advantages over conventional TKA. Future studies should explore the long-term clinical and cost-effectiveness of RaTKA in obese patients.
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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 | 0.001 | 0.001 |
| 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".