Intraoperative measurement of acetabular component position using imageless navigation during revision total hip arthroplasty
Bibliographic record
Abstract
Background: Acetabular component malposition is a major cause of dislocation following total hip arthroplasty (THA). Intellijoint HIP is an imageless navigation tool that has been shown to provide accurate intraoperative measurement of cup position during primary THA without substantially increasing operative time. However, its accuracy in revision THA has not been evaluated. This study therefore aims to assess the accuracy of Intellijoint HIP in measuring cup inclination and anteversion in comparison with computed tomography (CT) during revision THA. Methods: Intellijoint HIP was used to measure the position of the preexisting cup in 53 consecutive patients undergoing revision THA between December 2018 and February 2020. Two authors blinded to the intraoperative navigation measurements also independently measured cup position using preoperative CT according to Murray’s radiographic definitions. Pearson correlation coefficients with 95% confidence intervals (CIs), paired t tests and Bland–Altman plots were used to assess agreement between navigation- and CT-measured cup position. Statistical analysis was performed using GraphPad Prism, with p values less than 0.05 indicating statistical significance. Results: There was excellent agreement between navigation and CT measurements for both cup inclination (r = 0.89, 95% CI 0.81–0.93) and anteversion (r = 0.93, 95% CI 0.88–0.96), with the mean absolute difference being 5.2º (standard deviation [SD] 4.0º) for inclination and 4.8º (SD 5.4º) for anteversion. The navigation measurement was within 10º of the radiographic measurement in 47 of 53 (88.7%) cases for inclination and 46 of 53 (86.8%) cases for anteversion. Conclusion: Imageless navigation demonstrated excellent correlation and agreement with CT measurements for both inclination and anteversion over a wide range of acetabular component positions.
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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 | 0.001 | 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) | 0.000 | 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".