Bone Volumes and Trajectory Angles for Acetabular Anchor Placement Can Be Optimized
Bibliographic record
Abstract
Purpose The purpose of this study was to determine the optimal anchor placement and trajectory when repairing acetabular labral tears during hip arthroscopy with the primary focus on the 12 to 3 o’clock positions on the acetabular rim. Methods Three‐dimensional computational models of the pelvis were generated from 13 cadaveric specimens using 3D slicer medical imaging software. A set of cones, consistent with the dimensions of a commonly used sutured anchor, were virtually embedded into the models at the 12, 1, 2, and 3 o’clock positions around the acetabulum. Mirror images of the cone were extended toward the superficial aspect of the hip. The volume of bone occupied by the virtual anchor, the trajectory angle, and the volume of overlap between adjacent anchor locations were calculated. Results Bone volume was significantly greater at the 1 o’clock position (4196.2 [1190.2] mm 3 ) compared with all other positions ( P < .001). The 3 o’clock position had the smallest volume (629.2 [180.0] mm 3 ) and was also significantly less than the 12 ( P < .001) and 2 o’clock ( P = .014) positions). The trajectory angle of 32.04 [5.05]°) at the 1 o’clock position was significantly greater compared with all other positions ( P < .001). The least amount of adjacent position overlap occurred between the 2 and 3 o’clock positions (.12 [.42] mm 3 ), and this was statistically smaller than the overlap between cones at the 12 and 1 o’clock positions (214.28 [251.88] mm 3 ; P = .029) and the 1 and 2 o’clock positions (139.51 [177.14] mm 3 ; P = .044). Conclusions Trajectory angles and the thickness of bone around the acetabulum were the greatest at the 12 to 1 o’clock positions, with the 1 o’clock position identified as that with the largest trajectory angle for safe anchor insertion. Clinical Relevance The use of a single, workhorse portal, for anchor insertion may not be recommended and careful selection of a portal allowing a direct approach should be used for anterior anchor insertion.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".