Robotic-assisted total hip arthroplasty using the direct anterior approach: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: While several prior reviews have attempted to assess outcomes of robotic-assisted total hip arthroplasty (RA-THA) compared to manual technique, their findings lack generalizability due to several surgical techniques concurrently assessed. Therefore, the purpose of this study was to assess radiographic, clinical and patient reported outcomes following robotic-assisted total hip arthroplasty using the direct anterior approach. Methods: MEDLINE, EMBASE and CENTRAL were searched from inception to March 8, 2025 for comparative studies comparing outcomes for RA-THA using the DAA compared to manual total hip arthroplasty (M-THA). Eligible levels of evidence were I to III. Intraoperative, radiographic, patient-reported outcomes (PROs) as well complications/reoperations were assessed. Meta-analysis was performed on outcomes reported across a minimum of three studies. Results: Twelve comparative studies (9938 hips) were included for analysis. Most RA-THAs were performed using the MAKO (76 %). Operative time was 14.92 min shorter in the M-THA group (p < 0.00001). Acetabular anteversion was 1.87° less in the RA-THA group (p = 0.0002), with meta-analysis demonstrating no significant differences across acetabular inclination or leg length discrepancy. Patient reported outcomes mostly demonstrated no significant differences across groups. RA-THA demonstrated a non-significant 43 % reduction in overall complications (p = 0.28), but a 75 % significant reduction in reoperations (p = 0.02). Conclusion: RA-THA using the DAA does not lead to clinically significant improvements in acetabular component positioning, with potentially reduced rates of reoperations compared to M-THA. A notable disadvantage of RA-THA was prolonged operative time. Benefits in PROs were lacking with the strength of findings being limited by low levels of evidence and heterogenous instruments. Future high-quality trials with appropriately selected radiographic and patient reported outcomes are warranted. Clinically relevant outcomes to consider include the attainment of preoperative target component positioning, restoration of native patient biomechanics, as well as robot-specific complications. Level of evidence: III.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.018 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".