Can Surgeons Identify ACL Femoral Ridges Landmark and Optimal Tunnel Position? A 3D Model Study
Bibliographic record
Abstract
Purpose To examine the ability of surgeons to identify the osseous landmarks associated with the femoral anterior cruciate ligament (ACL) footprint and locate optimal tunnel placement on 3‐dimensional (3D) printed models compared with intraoperative placement. Methods Twelve sports fellowship‐trained orthopaedic surgeons were asked to identify a femoral landmark and an ACL footprint on 10 different 3D printed knees. The 3D models were made based on 20 real patients with different anatomical morphology who later received ACL reconstructive surgery using independent drilling. ImageJ software was used to quantify the measurements, which were then analyzed using descriptive statistics. Results Overall, none of the surgeons were able to consistently identify the junction of the bony ridges. The mean error per participant ranged from 2.81 to 7.34 mm in the proximal direction ( P = 3.30e‐05) and from 2.42 to 8.05 mm in the posterior direction ( P =4.88e‐12). None of the surgeons were able to appropriately identify the center of the femoral footprint on the anatomic 3D models. The difference between the center of the footprint surgeons identified on the 3D model and the tunnel graft location in surgery was significantly different ( P = .0046). On average, the magnitude of the error when the surgeons performed the actual surgery was 3.72 ± 2.43 mm, whereas on the 3D models it was 5.82 ± 1.97 mm. Conclusions Experienced sports fellowship‐trained orthopaedic surgeons were unable to correctly identify the junction of the intercondylar and bifurcate ridges and the native ACL footprint on 3D models. Operatively placed tunnels were more accurate implying that looking either through a scope or soft‐tissue landmarks play a significant role in surgeons ACL footprint localization. Clinical Relevance The graft position for ACL reconstruction plays an important role on the kinematics of the knee. This paper shows that soft tissue landmarks are needed to provide reliable reference points for reconstruction.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".