Patient Satisfaction Outcomes after Robotic Arm-Assisted Total Knee Arthroplasty: A Short-Term Evaluation
Bibliographic record
Abstract
Abstract Robotic arm-assisted total knee arthroplasty (RATKA) presents a potential, new added value for orthopedic surgeons. In today's health care system, a major determinant of value can be assessed by patient satisfaction scores. Therefore, the purpose of the study was to analyze patient satisfaction outcomes between RATKA and manual total knee arthroplasty (TKA). Specifically, we used the Western Ontario and McMaster Universities Arthritis Index (WOMAC) to compare (1) pain scores, (2) physical function scores, and (3) total patient satisfaction outcomes in manual and RATKA patients at 6 months postoperatively. In this study, 28 cemented RATKAs performed by a single orthopedic surgeon at a high-volume institution were analyzed. The first 7 days were considered as an adjustment period along the learning curve. Twenty consecutive cemented RATKAs were matched and compared with 20 consecutive cemented manual TKAs performed immediately. Patients were administered a WOMAC satisfaction survey at 6 months postoperatively. Satisfaction scores between the two cohorts were compared and the data were analyzed using Student's t-tests. A p-value < 0.05 was used to determine statistical significance. The mean pain score, standard deviation (SD), and range for the manual and robotic cohorts were 5 ± 3 (range: 0–10) and 3 ± 3 (range: 0–8, p < 0.05), respectively. The mean physical function score, SD, and range for the manual and robotic cohorts were 9 ± 5 (range: 0–17) and 4 ± 5 (range, 0–14, p = 0.055), respectively. The mean total patient satisfaction score, SD, and range for the manual and robotic cohorts were 14 points (range: 0–27 points, SD: ± 8) and 7 ± 8 points (range: 0–22 points, p < 0.05), respectively. The results from this study further highlight the potential of this new surgical tool to improve short-term pain, physical function, and total satisfaction scores. Therefore, it appears that patients who undergo RATKA can expect better short-term outcomes when compared with patients who undergo manual TKA.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".