Tibial Tunnel Placement in ACL Reconstruction Using a Novel Grid and Biplanar Stereoradiographic Imaging
Bibliographic record
Abstract
Background: Nonanatomic graft placement is a frequent cause of anterior cruciate ligament reconstruction (ACLR) failure, and it can be attributed to either tibial or femoral tunnel malposition. To describe tibial tunnel placement in ACLR, we used EOS, a low-dose biplanar stereoradiographic imaging modality, to create a comprehensive grid that combines anteroposterior (AP) and mediolateral (ML) coordinates. Purpose: To (1) validate the automated grid generated from EOS imaging and (2) compare the results with optimal tibial tunnel placement. Study Design: Descriptive laboratory study. Methods: Using EOS, 3-dimensional models were created of the knees of 37 patients who had undergone ACLR. From the most medial, lateral, anterior, and posterior points on the tibial plateau of the EOS 3-dimensional model for each patient, an automated and personalized grid was generated from 2 independent observers’ series of reconstructions. To validate this grid, each observer also manually measured the ML and AP distances, the medial proximal tibial angle (MPTA), and the tibial slope for each patient. The ideal tibial tunnel placement, as described in the literature, was compared with the actual tibial tunnel grid coordinates of each patient. Results: The automated grid metrics for observer 1 gave a mean (95% CI) AP depth of 54.7 mm (53.4-55.9), ML width of 75.0 mm (73.3-76.6), MPTA of 84.9° (83.7-86.0), and slope of 7.2° (5.4-9.0). The differences with corresponding manual measurements were means (95% CIs) of 2.4 mm (1.4-3.4 mm), 0.5 mm (–1.3 to 2.2 mm), 1.2° (–0.4° to 2.9°), and –0.4° (–2.1° to 1.2°), respectively. The correlation between automated and manual measurements was r = 0.78 for the AP depth, r = 0.68 for the ML width, r = 0.18 for the MPTA, and r = 0.44 for the slope. The center of the actual tibial aperture on the plateau was a mean of 5.5 mm (95% CI, 4.8-6.1 mm) away from the referenced anatomic position, with a tendency toward more medial placement. Conclusion: The automated grid created using biplanar stereoradiographic imaging provided a novel, precise, and reproducible description of the tibial tunnel placement in ACLR. Clinical Relevance: This technique can be used during preoperative planning, intraoperative guidance, and postoperative evaluation of tibial tunnel placement in ACLR.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".