Imageless Navigation Accurately Measures Component Orientation during Total Hip Arthroplasty: A Comparison with Postoperative Radiographs
Bibliographic record
Abstract
Abstract Accurate placement of acetabular components during total hip arthroplasty (THA) is paramount in ensuring long-term stability. Current methods for monitoring cup position and leg length intraoperatively are lacking due to susceptibility to inaccuracy or prohibitive cost. The purpose of this study was to evaluate the ability of an imageless surgical navigation tool to accurately measure acetabular cup inclination and leg length differential during THA. The authors retrospectively reviewed the medical records of patients who underwent primary or revision THA (posterolateral approach) at their facility with the assistance of computer-assisted navigation between February 2016 and March 2017. Pre- and postoperative radiographs were analyzed for leg length discrepancies and acetabular cup inclination. Radiographic values were compared with intraoperative values provided by the surgical navigation tool. The mean difference between inclination as measured from radiographs (44.4 ± 5.9 degrees) and navigation (43.0 ± 4.4 degrees) was −1.4 ± 4.6 degrees (mean absolute difference: 3.8 ± 2.8 degrees). Seventy-seven percent (48/62) of navigation measurements were within 5 degrees of radiographs. The mean difference between radiographic (7.39 ± 5.67 mm) and navigation (7.44 ± 4.81 mm) measurements of leg length differential was 0.29 ± 4.20 mm (mean absolute difference: 3.20 ± 2.69 mm). Navigation tool measurements were within 5 mm of radiographic values in 85% (39/46) of cases. At 90 days, idiopathic dislocation requiring revision surgery occurred in one patient (1.2%) with one additional patient (1.2%) requiring revision surgery due to a traumatic injury (fall). Computer-assisted navigation provided accurate intraoperative data regarding inclination and changes in leg length and was associated with a low rate of dislocation and revision surgery at 90-day follow-up.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".