Tibiofemoral Kinematics following Successful Anterior Cruciate Ligament Reconstruction Using Dynamic Multiple Resonance Imaging
Bibliographic record
Abstract
BACKGROUND: The aim of anterior cruciate ligament reconstruction is to reduce excess joint laxity, hoping to restore normal tibiofemoral kinematics and therefore improve joint stability. It remains unclear if successful ACL reconstruction restores normal tibiofemoral kinematics and whether it is this that is associated with a good result. STUDY: Case series. PURPOSE: To assess the kinematics of the anterior cruciate ligament-reconstructed knee using open-access MRI. METHODS: Tibiofemoral motion was assessed using open-access MRI, weightbearing through the arc of flexion from 0 degrees to 90 degrees in 10 patients with isolated reconstruction of the anterior cruciate ligament (hamstring autograft) in one knee and a normal contralateral knee. Midmedial and midlateral sagittal images were analyzed in all positions of flexion in both knees to assess the tibiofemoral relationship. Sagittal laxity was also assessed by performing the Lachman test while the knees were scanned dynamically using open-access MRI. RESULTS: The amount of excursion between the tibial and femoral joint surfaces was similar between the normal and reconstructed knees, but the relationship of tibia to femur was always different for each position of knee flexion assessed-the lateral tibia being about 5 mm more anterior in the anterior cruciate ligament-reconstructed knees. This anterior tibial position is statistically significantly different at 0 degrees (P <.0006), 20 degrees (P =.0004), 45 degrees (P =.002), and 90 degrees of flexion (P <.006). Anteroposterior laxity was similar between normal and anterior cruciate ligament-reconstructed knees. CONCLUSION: Anterior cruciate ligament reconstruction reduces sagittal laxity to within normal limits but does not restore normal tibiofemoral kinematics despite a successful outcome.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".