CT-based, robotic-arm assisted total hip arthroplasty (Mako) through anterior approach provides improved cup placement accuracy but no difference in clinical outcomes when compared to conventional technique
Bibliographic record
Abstract
BACKGROUND: With the restoration of the natural hip biomechanics, a successful total hip arthroplasty (THA) and long-term survival is pursued. Although robotic THA (rTHA) has been developed to increase accuracy of implant positioning, leg lengths and offsets, discussions about its radiological and clinical advantages over conventional THA (cTHA) continues. OBJECTIVE: The aim of this study was to compare clinical and radiological outcomes of robotic and conventional THA. METHODS: This retrospective study compares functional and radiological outcomes of 82 rTHA with a matched group of 82 cTHA in terms of age, sex, body mass index and preoperative functional scores. The minimum follow up was 12 months for all patients. Functional outcomes were Harris Hip Score (HHS) and the Western Ontario and McMaster University Osteoarthritis index (WOMAC) evaluated pre- and postoperatively. Radiological evaluations included position of cup placement according to Lewinnek and Callanan safe zones, Canal Fill Ratio (CFR), Leg Length Discrepancy (LLD), Lateral offset (LO) and Femoral Component Alignment (FCA). Complications were also evaluated. RESULTS: In the rTHA group, 91.5% (75 out of 82) of the acetabular cups were positioned within the safe zone whereas it was 63.4% (52 out of 82) for the cTHA group (p< 0.001). According to Callanan, they were 84.1% and 50%, respectively (p< 0.001). Between the groups, no statistically significant difference was found in CFR, LLD, HO, FCA, AI, AA, WOMAC, HHS and major complication rates. CONCLUSION: rTHA is superior to cTHA in terms of accuracy and reproducibility of the cup placement, however no apparent clinical benefit was found in short term follow.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".