Fluoroscopy Versus Imageless Optical Navigation in Direct Anterior Approach Total Hip Arthroplasty
Bibliographic record
Abstract
BACKGROUND: Utilization of the direct anterior approach (DAA) for total hip arthroplasty (THA) has risen in popularity. Accurate implant placement is a critical factor that influences patient outcomes. The goal of this study was to compare the accuracy and precision of fluoroscopy with imageless optical navigation in DAA THA. METHODS: This was a cohort study of 640 consecutive primary DAA THAs performed with intraoperative fluoroscopy (n = 300 patients) or imageless optical navigation (n = 304 patients). Accuracy was compared by measuring acetabular cup inclination, anteversion, and leg-length discrepancy (LLD). The proportion of implants placed within the Lewinnek safe zone and those placed within a more precise target of 40 ± 5° inclination and 20 ± 5° anteversion was evaluated. RESULTS: According to the Lewinnek criteria, there was no difference in the percentage of implants placed within both inclination and anteversion targets (fluoroscopy: 90.3%; navigation: 88.8%, P = 0.519). Using the more precise targets, navigation increased the implants positioned correctly for both inclination and anteversion (fluoroscopy: 50.3%; navigation: 65.6%, P < 0.001). Navigation increased the proportion of implants positioned within the target anteversion zone (fluoroscopy: 71.3%; navigation: 83.8%, P < 0.001) but not inclination (fluoroscopy: 71.9%; navigation: 76.9%, P = 0.147). The mean LLD was higher with the use of fluoroscopy compared with navigation (5.5 mm, standard deviation: 4.1; 4.6 mm, SD: 3.4, P < 0.005). No difference in dislocation rate was observed ( P = 0.643). CONCLUSION: Both fluoroscopy and imageless optical navigation demonstrated accurate acetabular implant positioning during DAA THA. Navigation was more precise and associated with improved acetabular anteversion placement and restoration of LLD. Navigation is an accurate alternative to fluoroscopy with decreased radiation exposure.
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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 | 0.011 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".