A Cadaver Study to Evaluate the Accuracy of a New 3D Mini-Optical Navigation Tool for Total Hip Arthroplasty.
Bibliographic record
Abstract
BACKGROUND: Accurate measurement of acetabular cup position (CP), changes in leg length (LL), and offset (OS) are paramount in ensuring proper sizing and implantation of components during total hip arthroplasty (THA). LL/OS inaccuracies can cause low back pain, neurological deficits, and patient dissatisfaction, while inaccurate positioning of the acetabular cup can lead to instability, dislocation, and, ultimately, revision surgery. The objective of this study was to evaluate the accuracy of a mini-navigation tool in measuring CP and LL/OS differential during THA. MATERIALS AND METHODS: Three board-certified orthopedic surgeons each performed four THA procedures via the posterior approach on six cadavers (12 hips) utilizing a novel mini-navigation tool. Imaging included pre- and post-operative radiographs and post-operative CT scans. Image analysis was performed by two radiologists not involved in the surgical procedures. System accuracy regarding measurement of cup position (anteversion and inclination) was determined by comparing the CT measurement of cup orientation with data gathered intraoperatively by probing the face of the implanted cup with the navigation tool and recording the coordinates. RESULTS: The mean absolute difference between CT and device measurements of cup position was 0.74º (SD: 0.47, range: 0.19-1.48) for anteversion and 0.97º (SD: 0.67, range: 0.27-2.57) for inclination. The mean difference between device and radiograph measurements of LL changes was 0.27 mm (SD: 3.61, range: -5.20-7.78) (absolute mean: 2.71±2.25 mm), while the mean difference in OS was 1.75 mm (SD: 3.00, range: -2.47-6.65) (absolute mean: 2.37±2.44 mm). CONCLUSIONS: This novel mini-navigation tool measured CP, LL, and OS accurately when compared with implant position measured on imaging.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".