Robotic-assisted total knee arthroplasty with MAKO is associated with improved functional outcomes
Bibliographic record
Abstract
Aims: To improve functional outcomes following total knee arthroplasty (TKA), robotic systems have been introduced such as the MAKO (Stryker), the most widely used system globally at present. This systematic review aimed to compare the patient-reported outcome measures (PROMs) of robotic TKA (RTKA) to manual TKA (MTKA). Methods: Five electronic databases were systematically searched for eligible articles that used PROMs to compare MAKO RTKA to MTKA. The primary outcome was the Forgotten Joint Score (FJS). We defined follow-up periods as short (up to three months), medium (three months to one year), and long term (beyond one year). We pooled outcomes combining the Knee Society Scoring System (KSS), Oxford Knee Score (OKS), and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Meta-analyses were conducted using a random-effects model and reported using mean difference (MD) or standardized mean difference (SMD) and 95% CI. Results: In total, 22 articles evaluating 3,738 TKAs were included: 1,835 MTKAs and 1,903 RTKAs. The evidence level for most studies were IIa, due to few high-level studies. Using FJS, meta-analysis showed little difference at short-term follow-up (MD 11.49, 95% CI -5.62 to 28.59), but found a difference at medium-term follow-up (MD 5.50, 95% CI 2.19 to 8.81). This was not sustained at long-term follow-up (MD 23.89, 95% CI -16.50 to 64.27). Pooling all PROMs showed no difference in the short term (SMD 0.27, 95% CI -0.05 to 0.59), but results favoured RTKA at medium- (SMD 0.46, 95% CI 0.22 to 0.70) and long-term follow-up (SMD 0.40, 95% CI 0.13 to 0.66). Conclusion: There are few high-level studies, but based on current data MAKO RTKA may result in improved functional outcomes compared to MTKA. Further randomized controlled trials are required to provide robust data and to assess clinical and cost-effectiveness as well as a wider spectrum of early and late 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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".