Evaluating the Accuracy of Tibial Tunnel Placement After Anatomic Single-Bundle Anterior Cruciate Ligament Reconstruction
Bibliographic record
Abstract
BACKGROUND: Anatomic anterior cruciate ligament (ACL) reconstruction improves knee kinematics and joint stability in symptomatic patients who have ACL deficiency. Despite a concerted effort to place the graft within the ACL's native attachment sites, the accuracy of tunnel placement using contemporary techniques is not well established. PURPOSE: To use 3-dimensional magnetic resonance imaging (3D MRI) to prospectively evaluate the accuracy of tibial tunnel placement after anatomic ACL reconstruction. STUDY DESIGN: Case series; Level of evidence, 4. METHODS: Forty patients with symptomatic, ACL-deficient knees were prospectively enrolled in the study and underwent 3D MRI of both their injured and uninjured knees before and after surgery through use of a validated imaging protocol. The root ligament of the anterior horn of the lateral meniscus was used as a radiographic reference, and the center of the reconstructed graft was compared with that of the contralateral normal knee. The tunnel angles and intra-articular graft angles were also measured, as was the percentage overlap between the native tibial footprint and tibial tunnel. RESULTS: < .001) posterior to the native ACL footprint. The mean distance between the center of the native and reconstructed ACL at the tibial attachment site was 6.24 mm. Of the 40 patients, 18 patients had a tibial tunnel that overlapped more than 50% of the native footprint, and 10 patients had maximal (100%) overlap. Further, 22 of the 40 patients had less than 50% overlap with the native footprint, and in 12 patients the footprint was missing completely. CONCLUSION: Despite the use of contemporary surgical techniques to perform anatomic ACL reconstruction, a significant positioning error in tibial tunnel placement remains.
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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 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".