Comparison of Postoperative Pain and Function in Robotic Total Knee Arthroplasty and Conventional Total Knee Arthroplasty Amongst Patients at King Fahad Medical City in Riyadh, Saudi Arabia
Bibliographic record
Abstract
Background Total knee arthroplasty (TKA) is the definitive surgical treatment for end-stage osteoarthritis and has been proven to relieve pain and improve function. With the rise in demand and the number of TKA procedures every year, more studies have been conducted on robotic TKA. Objective The objective of this study is to compare the postoperative pain between robotic and conventional TKA and the postoperative functional level between robotic and conventional TKA. Method This is a quantitative, observational, prospective study conducted from February 2022 to August 2022 amongst patients in the orthopaedic department of King Fahad Medical City, Riyadh, Saudi Arabia, who have undergone primary TKA for end-stage osteoarthritis using robotic TKA and conventional TKA. After applying the exclusion and inclusion criteria, a total of 26 patients (12 robotic and 14 conventional) were included in the study. The patients were assessed at three time points: two weeks, six weeks, and three months post-op. They were assessed using the Western Ontario and McMaster Universities Arthritis Index (WOMAC) score and the visual analogue scores (VAS) used to assess pain. Result A total of 26 patients were included in this research. The patients were divided into two groups: 12 robotic TKA patients and 14 conventional TKA patients. In this study, while comparing patients who underwent robotic TKA with those who underwent conventional TKA, no statistical significance was found regarding pain and function at all stages postoperatively. Conclusion There was no short-term difference between robotic and conventional TKA regarding pain and function. There is a need for further extensive research on robotic TKA in terms of cost-effectiveness, complications, implant survivorship, and long-term outcomes.
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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 | 0.000 | 0.001 |
| 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".