Quantitative Evaluation of Lateral Articular Cartilage Morphology on Magnetic Resonance Imaging at 2-years following Anterior Cruciate Ligament Reconstruction with Or Without A Lateral Extra-Articular Tenodesis
Bibliographic record
Abstract
Objectives: The purpose of this study is to evaluate whether augmentation of anterior cruciate ligament reconstruction (ACLR) with a lateral extra-articular tenodesis (LET) affects articular cartilage morphology on magnetic resonance imaging (MRI) two years post-operatively in a young, active population. Methods: Ninety-seven patients at the Fowler Kennedy Sports Medicine Clinic participating in the Stability study, a multi-center randomized controlled trial of 618 patients comparing ACLR ± LET, underwent bilateral 3T MRI at two-years post-surgery. Quantitative MRI (qMRI) pulse sequences consist of a Sagittal Multi-Echo Spin Echo T2 Mapping sequence, and a 16-shot Gradient Echo T1rho Mapping sequence. The primary imaging outcome was T1rho and T2 relaxation time. Articular cartilage was manually segmented on three consecutive load-bearing slices by three independent raters, and values were averaged for three regions on the tibia and five regions on the femur in both the medial and lateral compartments. Raters were blind to scan order and an ICC (2,1) was used to assess inter-rater reliability. Independent t-tests were used to compare relaxation times between groups in each compartment. Results: Ninety-seven participants (47 ACL, 50 ACL+LET), including 59.7% (58/97) females with a mean age of 18.9 years underwent MRI 2-years post-op (range = 20 to 36 months). Currently, 41 scans (23 ACL, 18 ACL+LET) have been analyzed. There were no significant differences between groups for regions in the medial compartment. T2 relaxation times were significantly higher for the ACL+LET group in the central and posterolateral (PL) tibia and the PL femur (Table 1). T1rho relaxation times were significantly lower for the ACL+LET group in the proximal posterior femur. Raters showed good inter-rater reliability with an ICC of 0.85.. Conclusions: T2 relaxation times are elevated in the lateral compartment following ACLR augmented by LET compared to ACLR alone however, the clinical significance is not currently clear as the differences were small (<4ms). Also, the presence of a metal staple may influence the relaxation times calculated. However, the potential that a LET may lead to early cartilage degeneration cannot be discounted and as such should be monitored in the long-term.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".