Intellijoint HIP®: a 3D mini-optical navigation tool for improving intraoperative accuracy during total hip arthroplasty
Bibliographic record
Abstract
Wayne G Paprosky,1,2 Jeffrey M Muir3 1Department of Orthopedics, Section of Adult Joint Reconstruction, Department of Orthopedics, Rush University Medical Center, Rush–Presbyterian–St Luke’s Medical Center, Chicago, 2Central DuPage Hospital, Winfield, IL, USA; 3Intellijoint Surgical, Inc, Waterloo, ON, Canada Abstract: Total hip arthroplasty is an increasingly common procedure used to address degenerative changes in the hip joint due to osteoarthritis. Although generally associated with good results, among the challenges associated with hip arthroplasty are accurate measurement of biomechanical parameters such as leg length, offset, and cup position, discrepancies of which can lead to significant long-term consequences such as pain, instability, neurological deficits, dislocation, and revision surgery, as well as patient dissatisfaction and, increasingly, litigation. Current methods of managing these parameters are limited, with manual methods such as outriggers or calipers being used to monitor leg length; however, these are susceptible to small intraoperative changes in patient position and are therefore inaccurate. Computer-assisted navigation, while offering improved accuracy, is expensive and cumbersome, in addition to adding significantly to procedural time. To address the technological gap in hip arthroplasty, a new intraoperative navigation tool (Intellijoint HIP®) has been developed. This innovative, 3D mini-optical navigation tool provides real-time, intraoperative data on leg length, offset, and cup position and allows for improved accuracy and precision in component selection and alignment. Benchtop and simulated clinical use testing have demonstrated excellent accuracy, with the navigation tool able to measure leg length and offset to within <1 mm and cup position to within <1° in both anteversion and inclination. This study describes the indications, procedural technique, and early accuracy results of the Intellijoint HIP surgical tool, which offers an accurate and easy-to-use option for hip surgeons to manage leg length, offset, and cup position intraoperatively. Keywords: total hip arthroplasty, leg length, offset, cup position, Intellijoint HIP, intraoperative
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".