A new mini-navigation tool allows accurate component placement during anterior total hip arthroplasty
Bibliographic record
Abstract
Introduction: Computer-assisted navigation systems have been explored in total hip arthroplasty (THA) to improve component positioning. While these systems traditionally rely on anterior pelvic plane registration, variances in soft tissue thickness overlying anatomical landmarks can lead to registration error, and the supine coronal plane has instead been proposed. The purpose of this study was to evaluate the accuracy of a novel navigation tool, using registration of the anterior pelvic plane or supine coronal plane during simulated anterior THA. Methods: Measurements regarding the acetabular component position, and changes in leg length and offset were recorded. Benchtop phantoms and target measurement values commonly seen in surgery were used for analysis. Measurements for anteversion and inclination, and changes in leg length and offset were recorded by the navigation tool and compared with the known target value of the simulation. Pearson’s r assessed the relationship between the measurements of the device and the known target values. Results: The device accurately measured cup position and leg length measurements to within 1° and 1 mm of the known target values, respectively. Across all simulations, there was a strong, positive relationship between values obtained by the device and the known target values ( r =0.99). Conclusion: The preliminary findings of this study suggest that the novel navigation tool tested is a potentially viable tool to improve the accuracy of component placement during THA using the anterior approach. Keywords: total hip arthroplasty, computer-assisted navigation, anterior approach, accuracy, anterior pelvic plane, supine coronal plane
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".