Patients undergoing robotic arm-assisted total knee arthroplasty have a greater improvement in knee-specific pain but not in function
Bibliographic record
Abstract
Aims: The aim was to assess whether robotic-assisted total knee arthroplasty (rTKA) had greater knee-specific outcomes, improved fulfilment of expectations, health-related quality of life (HRQoL), and patient satisfaction when compared with manual TKA (mTKA). Methods: A randomized controlled trial was undertaken (May 2019 to December 2021), and patients were allocated to either mTKA or rTKA. A total of 100 patients were randomized, 50 to each group, of whom 43 rTKA and 38 mTKA patients were available for review at 12 months following surgery. There were no statistically significant preoperative differences between the groups. The minimal clinically important difference in the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score was defined as 7.5 points. Results: There were no clinically or statistically significant differences between the knee-specific measures (WOMAC, Oxford Knee Score (OKS), Forgotten Joint Score (FJS)) or HRQoL measures (EuroQol five-dimension questionnaire (EQ-5D) and EuroQol visual analogue scale (EQ-VAS)) at 12 months between the groups. However, the rTKA group had significantly (p = 0.029) greater improvements in the WOMAC pain component (mean difference 9.7, 95% confidence interval (CI) 1.0 to 18.4) over the postoperative period (two, six, and 12 months), which was clinically meaningful. This was not observed for function (p = 0.248) or total (p = 0.147) WOMAC scores. The rTKA group was significantly (p = 0.039) more likely to have expectation of 'Relief of daytime pain in the joint' when compared with the mTKA group. There were no other significant differences in expectations met between the groups. There was no significant difference in patient satisfaction with their knee (p = 0.464), return to work (p = 0.464), activities (p = 0.293), or pain (p = 0.701). Conclusion: Patients undergoing rTKA had a clinically meaningful greater improvement in their knee pain over the first 12 months, and were more likely to have fulfilment of their expectation of daytime pain relief compared with patients undergoing mTKA. However, rTKA was not associated with a clinically significant greater knee-specific function or HRQoL, according to current definitions.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".