Accuracy of imageless navigation for functional cup positioning and restoration of leg length in total hip arthroplasty: a matched comparative analysis
Bibliographic record
Abstract
Background: Computer-assisted navigation has the potential to improve the accuracy of cup positioning during total hip arthroplasty (THA) and prevent leg-length discrepancy (LLD). The purpose of this study was to compare acetabular cup position and postoperative LLD after primary THA using posterolateral approach. Methods: Between August 2016 to December 2017, 57 THAs using imageless navigation were matched with 57 THA without navigation, based on age, gender, and BMI. Postoperative weight-bearing radiographs were assessed for anteversion, inclination, and LLD. Functional LLD was measured in comparison to the contralateral side. The proportion of cups within Lewinnek’s safe zone and LLD greater than 5 mm were assessed. Results: The mean age was 54.9±9.6 yr and 57.6±12.5 yr in control and navigated groups, respectively. Mean cup orientation in the navigated group was 20.6±3.3 degrees (17 to 25) of anteversion and 41.9±4.8 degrees (30 to 51) of inclination, versus 25±11.1 degrees (10 to 31) and 45.7±8.7 degrees (29 to 55) in the control group; these were statistically significant (P=0.005 and P<0.001, respectively). In the navigated group, significantly more acetabular cups were placed within Lewinnek’s safe zone (anteversion: 77% vs. 47%, P=0.005; inclination: 91% vs. 67%, P<0.001). There was no significant difference in mean LLD in the navigation and control groups (3.2±1.5 mm vs. 4.6±3.4 mm, P=0.36), although fewer LLDs of greater than 5 mm were reported in the navigated group (7.1%) than in the control group (31.6%, P=0.007). Conclusions: The use of imageless computer-assisted navigation improved the accuracy of acetabular cup components and LLD. Level of Evidence: Level III.
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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 | 0.000 |
| 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".