Effect of digital navigation during anterior-approach total hip arthroplasty on patient outcomes
Bibliographic record
Abstract
Background: Fluoroscopic guidance is a well-established technique in anterior-approach total hip arthroplasty (AA-THA) to enhance appropriate implant size and positioning, and to address potential complications, such as leg-length discrepancy (LLD). The primary objective of this study was to assess whether the implementation of a noninvasive fluoroscopy-assisted digital-navigation software improved the accuracy of postoperative leg length in AA-THA. Methods: We conducted a retrospective cohort study involving patients who were identified from the prospectively collected patient-level provincial administrative data repositories. We extracted outcome data and measured leg lengths on postoperative radiographs. Results: A total of 435 patients were identified. After exclusion, 400 patients were included in the study, with 247 in the navigation-software cohort. There was no significant difference between the groups with respect to demographic data. In the navigation-software cohort, we found no significant difference between the achieved LLD and zero LLD, whereas we found a significant difference in the fluoroscopy-only cohort (p < 0.001). The navigation-software cohort required more fluoroscopy time and significantly reduced case time. Conclusion: The use of navigation software in AA-THA is safe and effective to achieve more accurate postoperative leg length. Despite the increase in fluoroscopic time, there was a small but statistically significant reduction in operative time. The value of such technology would be expected to substantially increase if used during the learning curve when introducing AA-THA to contemporary practice.
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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 | 0.009 |
| 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".