A minimally invasive periacetabular osteotomy technique: minimizing intraoperative risks
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The periacetabular osteotomy (PAO) is an extensive surgical procedure associated with potential risk to the adjacent neurovascular structures. A steep learning curve exists, with surgeon experience an important factor in outcome. Little detail exists of the osteotomies themselves, and how to make them safe and reproducible. This article describes our PAO technique with emphasis on specific safety steps. When performing the posterior column cut, migration of the osteotome beyond the lateral pelvis may lead to damage of the sciatic nerve. The safety features detailed include novel measurement of the posterior column width and the use of specific-width osteotomes to complete this osteotomy. To plan the cut, several computerized tomography-based measurements are taken starting just above the greater sciatic notch and continuing down to the inferior part of the acetabulum. The angle of this cut is determined by acetabular morphology and the width of the posterior column. These posterior column width measurements will determine the width of the osteotomes used to perform the cut with little risk that an osteotome will penetrate too far on the lateral side of the pelvis. To ensure the lateral cortex has been cut completely proximally, an osteotome with pre-measured depths may be used from a medial to a direct lateral trajectory. The senior author has been performing this modified approach since 2010 (n = 530 PAOs) and has witnessed no vascular injuries and no nerve injuries aside from minor lateral femoral cutaneous nerve issues. Utilization of these techniques has prevented any major nerve injury without the need for intraoperative electromyography.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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 it