Robotic-Assisted versus Manually Implanted Total Hip Arthroplasty: A Clinical and Radiographic Comparison.
Bibliographic record
Abstract
INTRODUCTION: Component positioning during THA is one of the more critical surgeon-controlled factors as malposition has been associated with higher rates of hip dislocations, poor biomechanics, accelerated wear rates, leg length discrepancies (LLDs), and revision surgeries. In order to reduce the rates of component malposition and improve surgical accuracy, robotic-assisted THA has developed increased interest. The primary objective of this study was to compare patient outcomes following THA using the Mako Stryker robotic system (Stryker Orthopaedics, Mahwah, New Jersey) to outcomes in patients who underwent conventional instrumented THA. MATERIALS AND METHODS: Consecutive patients undergoing THA with a direct-lateral surgical approach from a single surgeon were reviewed. Patients were treated with either a robotic-arm assisted total hip arthroplasty (RTHA) or a conventional-instrumented total hip arthroplasty (CTHA). Minimum follow up was 16 months. RESULTS: Robotic-assisted THA significantly improved patient outcomes compared to conventional THA. No significant differences were observed in postoperative radiographic outcomes between the RTHA and CTHA cohorts. In our analysis, patients in the RTHA cohort compared to the CTHA cohort had significantly higher Western Ontario and McMaster Universities Arthritis Index (WOMAC) (P<0.001) and Harris Hip Scores (P<0.05) at final follow up. There were no significant differences between the RTHA cohort and CTHA cohorts in regard to cup inclination (°) (P=0.10), hip length difference (mm) (P=0.80), hip length discrepancy (mm) (P=0.10), and global offset difference (mm) (P=0.20). CONCLUSION: Further studies, particularly prospective randomized studies, are necessary to investigate the short- and long-term clinical outcomes, possible long-term complications, and cost-effectiveness of robotic-assisted THA in regard to improving outcomes and accuracy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".