Reliability of Estimates of ACL Attachment Locations in 3-Dimensional Knee Reconstruction Based on Routine Clinical MRI in Pediatric Patients
Bibliographic record
Abstract
BACKGROUND: Current techniques of anterior cruciate ligament (ACL) reconstruction focus on the placement of femoral and tibial tunnels at anatomic ACL attachments, which can be difficult to identify intraoperatively. PURPOSE: To determine whether the 3-dimensional (3D) center of ACL attachments can be reliably detected from routine magnetic resonance imaging (MRI) in patients with intact ACLs and whether the reliability of this technique changes if the ACL is torn. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 3. METHODS: A computer technique was developed in which users identify points along ACL attachments on routine clinical MRI of preoperative knees. These attachments are then displayed on a 3D MRI reconstruction, which can be used as a visual guide for the surgeon during arthroscopic surgery. Thirty-seven pediatric patients (age range, 10-17 years) with ACL tears and 37 controls with intact ACLs were examined. Two blinded observers identified cruciate ligament attachments on routine clinical 1.5-T MRI of knees. From the resulting 3D model, the location of the center of each ligament attachment site and its area were calculated and reliability assessed. RESULTS: Mean interobserver variation of the centers of ACL attachments for the intact versus torn ACL was 1.7 ± 0.9 mm versus 1.8 ± 1.1 mm (femoral) and 1.4 ± 0.9 mm versus 1.7 ± 1.0 mm (tibial), respectively (P > .05). The 95% confidence interval for the center location was at most 4 mm. The identified ACL attachment areas were more variable, with interobserver reliability ranging from fair to excellent by the intraclass correlation coefficient. Overlap of ligament areas between observers for the intact versus torn ACL was 70% ± 15% versus 73% ± 12% (femoral) and 79% ± 9% versus 78% ± 10% (tibial), respectively (P > .05). In all cases, intraobserver reliability was superior to interobserver reliability. CONCLUSION: The 3D locations of ACL tibial and femoral attachment centers were identified from routine clinical MRI with variability averaging less than 2 mm between 2 observers. The margin of error was at most 4 mm, representing the thickness of a single axial MRI slice, whether the ACL was intact or torn. Remnant tissue at attachments allows a reliable assessment even of torn ligaments. Identification of the ligament attachment areas was more user dependent than was identification of the attachment centers.
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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 | 0.004 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".