POSTEROMEDIAL MENISCAL AND ANTERIOR CRUCIATE LIGAMENT STRAINS DURING DYNAMIC ACTIVITIES FOLLOWING ANTERIOR CRUCIATE LIGAMENT RECONSTRUCTION
Bibliographic record
Abstract
Background: Meniscal strain patterns are not well understood during dynamic activities. Furthermore, the impact of ACL reconstruction on meniscal strain has not been thoroughly investigated. The purpose of this study was to characterize ACL and meniscal strain during dynamic activities and investigate the strain difference between ACL-intact and ACL-reconstructed ligament conditions. Methods: ACL and medial meniscal strain were measured in-vitro during gait, a double leg squat, and a single leg squat. For each activity kinematics and muscle forces were applied to seven cadaveric specimens using a dynamic knee simulator. Testing was performed in the ACL-intact and ACL-reconstructed ligament conditions. Results: Both the ACL and meniscus had distinct strain patterns that were found to have a significant interaction with knee angle during gait and double leg squat ([Formula: see text]). During gait, both tissues experienced lower strain during swing than stance (ACL: 3.0% swing, 9.1% stance; meniscus: 0.2% swing, 1.3% stance). Meniscal strain was not found to be different between ACL-intact and ACL-reconstructed conditions ([Formula: see text]). Conclusions: During dynamic activities, the strain in the meniscus was not altered between ACL ligament conditions. This indicates that meniscal mechanics after ACL reconstruction are similar to a healthy knee. These results help further the understanding of osteoarthritis risk after ACL reconstruction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".