Accuracy of Image-less Navigation for Functional Cup Positioning in Total Hip Arthroplasty
Bibliographic record
Abstract
Computer-assisted navigation has the potential to improve the accuracy of cup positioning during total hip arthroplasty (THA) and prevent leg length discrepancy (LLD). The purpose of this study was to compare acetabular cup position and post- operative LLD after primary THA using posterolateral approach. Between August 2016 to December 2017, 57 THAs using imageless navigation were matched with 57 THA without navigation, based on age, gender and BMI. Post-operative weight-bearing radiographs were assessed using for anteversion, inclination and LLD. Goal for functional cup placement was 40° inclination and 20° anteversion based on preoperative weight bearing pelvic images. Functional LLD was measured as compared to pre- operative radiographs and contralateral side. Proportion of cups within Lewinnek’s safe zone, proximity to a pre-operative target of and the LLD >5 mm was assessed. The mean age was 54.9 ± 9.6 years (30 – 72) and 57.6 ± 12.5 years (20 – 85) in control and navigated groups, respectively. Mean cup orientation in the navigated group was 20.6°± 3.3° (17 - 25) of anteversion and 41.9°± 4.8° (30 - 51) of inclination, vs. 25.0°± 11.1° (10 - 31) and 45.7°± 8.7° (29 – 55) in control group, where were statistically significant (p=0.005 and p=0.0001), respectively. In the navigated group, significantly more acetabular cups were placed within Lewinnek’s safe zone (anteversion: 78% vs. 47%, p=0.005; inclination: 92% vs. 67%, p=0.002). There was no significant difference in mean LLD in navigation and control groups (3.1 ± 1.5 mm vs. 4.6 ± 3.4 mm, p=0.36), although fewer LLDs >5 mm were reported in the navigated group (7.1%) than in controls (31.4%, p=0.007). The use of this image-less computer-assisted navigation improved the accuracy with which acetabular cup components were placed and may represent an important method for limiting post-operative complications related to cup malpositioning and LLD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".