Accuracy of an adjustable patient-specific guide for acetabular alignment in hip replacement surgery (Optihip)
Bibliographic record
Abstract
Implant malalignment in hip arthroplasty increases the risk of revision surgery due to problems such as hip instability, wear, and impingement. Traditional instrumentation lacks accuracy and does not individualize the goal. Computer-assisted surgery (CAS) and patient-specific solutions improve accuracy but add considerably to the cost, amongst other drawbacks. We developed an adjustable mechanical device, called Optihip, which is set to a patient-specific goal preoperatively and is independent of pelvis position intraoperatively. The purpose of the present study was to evaluate Optihip's accuracy ex vivo. Acetabular components were implanted into six cadaveric specimens, 12 hips, by two surgeons, with the device individually adjusted according to preoperative templating on computed tomography (CT) images relative to defined acetabular rim landmarks; options also exist for templating on single or biplanar X-rays. Intraoperatively, the device was positioned on the corresponding anatomical landmarks allowing the insertion of a guide pin, which then defined the desired orientation of the acetabular cup during impaction. Mean absolute difference between the preoperatively planned cup alignment and final acetabular cup orientation, measured from postoperative CT images, was 2.5±1.2° for inclination and 2.5±2.2° for version with maximum values of 4.7° and 6.8°, respectively. Compared with previous in vivo reports, Optihip guided the acetabular cup orientation more accurately than conventional hip arthroplasty, and comparably to CAS or patient-specific systems, while fitting into the normal surgical workflow. Although clinical testing is required to confirm these experimental results, the positive ex vivo accuracy suggests good potential for improving revision rates and patient functional outcome.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".