Improving registration accuracy during total hip arthroplasty: a cadaver study of a new, 3-D mini-optical navigation system
Bibliographic record
Abstract
INTRODUCTION: Maintaining accuracy of component placement is an important step in ensuring the long-term stability of components during total hip arthroplasty (THA). Computer-assisted navigation has improved accuracy but errors associated with the registration process are known to impact the accuracy of final measurements. The purpose of this cadaver study was to determine the registration error associated with a novel mini-navigation system. METHODS: 3 board-certified orthopaedic surgeons performed 4 THA procedures each via the posterolateral approach on 6 cadavers (12 hips) using the mini-navigation tool. Pre- and post-operative radiographs and post-operative computed tomography (CT) images were obtained. Image analysis was performed by 2 radiologists not involved in the surgical procedures. During registration, surgeons aligned the alignment rod with the anterior pelvic plane (APP) to provide a reference plane for comparison with traditional navigation. Cup position from the device was compared with measurements gathered from post-op imaging. RESULTS: The mean difference between CT and device measurements for inclination was -1.7° (standard deviation [SD] 4.9°), while the mean absolute difference was 4.2° (SD 3.2°). The mean difference between anteversion angles calculated from CT scans and from the device was -3.5° (SD 4.5°), with an absolute difference of 4.0° (SD 4.0°). 100% (12/12) of inclination measurements and 92% (11/12) of anteversion measurements fell within both the clinical and statistical limits of agreement when analyzed via the Bland-Altman technique. CONCLUSIONS: This study demonstrates that the registration error associated with this new mini-navigation system compares favourably with the known registration error associated with traditional navigation systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".