Accuracy of Acetabular Component Positioning Using Computer-assisted Navigation in Direct Anterior Total Hip Arthroplasty
Bibliographic record
Abstract
Background Appropriate component positioning in total hip arthroplasty (THA) is imperative for long-term survivorship. C-arm fluoroscopy provides visual guidance in the direct anterior approach (DAA), but it is limited by qualitative properties. Conversely, imageless computer-assisted navigation systems (CAS) provide surgeons with intraoperative, three-dimensional (3D) quantitative measurements for cup position, although the accuracy of such systems has not been extensively addressed in the DAA. We evaluated the ability of an imageless CAS to deliver measurements for acetabular cup position with accuracy in the DAA. Materials and methods A retrospective analysis of 69 primary THA procedures was conducted. Acetabular cup position measurements (anteversion and inclination) obtained intraoperatively by imageless navigation were compared to standard, postoperative anteroposterior pelvic radiographic measurements. Statistical comparisons were made using the Bland-Altman technique. Results The mean difference between device and radiographic measurements for anteversion was 3.4° (standard deviation (SD): 4.1°; absolute mean difference (ABS): 4.2°), and 4.0° for inclination (SD: 3.6°; ABS: 4.3°). Bland-Altman analysis demonstrated excellent agreement; 93% (64/69) and 97% (67/69) of anteversion pairings fell within the statistical and clinical limits of agreement, whereas 94% (65/69) and 100% (69/69) of inclination pairings were within the statistical and clinical limits, respectively. Conclusions Measurements obtained intraoperatively for acetabular cup position using imageless navigation in the DAA are agreeable with the current clinical standard.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".