One-Year Patient Outcomes for Robotic-Arm-Assisted versus Manual Total Knee Arthroplasty
Bibliographic record
Abstract
Abstract Although there are many studies on the alignment advantages when using the robotic arm–assisted (RAA) system for total knee arthroplasty (TKA), there have been questions regarding patient-reported outcomes. Therefore, the purpose of this study was to use this index to compare: (1) total, (2) physical function, and (3) pain scores for manual versus RAA patients. We compared 53 consecutive RAA to 53 consecutive manual TKAs. No differences in preoperative scores were found between the cohorts. Patients were administered a modified Western Ontario and McMaster Universities Osteoarthritis Index satisfaction survey preoperatively and at 1-year postoperatively. The results were broken down to: (1) total, (2) physical function, and (3) pain scores. Univariate analysis with independent samples t-tests was used to compare 1-year postoperative scores. Multivariate models with stepwise backward linear regression were utilized to evaluate the associations between scores and surgical technique, age, sex, as well as body mass index (BMI). Statistical analyses were performed with a p < 0.05 to determine significance. The RAA cohort had significantly improved mean total (6 ± 6 vs. 9 ± 8 points, p = 0.03) and physical function scores (4 ± 4 vs. 6 ± 5 points, p = 0.02) when compared with the manual cohort. The mean pain score for the RAA cohort (2 ± 3 points [range, 0–14 points]) was also lower than that for the manual cohort (3 ± 4 points [range, 0–11 points]) (p = 0.06). On backward linear regression analyses, RAA was found to be significantly associated with more improved total (β coefficient [β] −0.208, standard error [SE] 1.401, p < 0.05), function (β = 0.216, SE = 0.829, p < 0.05), and pain scores (β −0.181, SE = 0.623, p = 0.063). The RAA technique was found to have the strongest association with improved scores when compared with age, gender, and BMI. This study suggests that RAA patients may have short-term improvements at minimum 1-year postoperatively. However, longer term follow-up with greater sample sizes is needed to further validate these results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 teacher head, 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".