In Vitro Assessment of Knee Joint Biomechanics Using a Virtual Anterior Cruciate Ligament Reconstruction
Bibliographic record
Abstract
Understanding the biomechanical impact of injuries and reconstruction of the anterior cruciate ligament (ACL) is vital for improving surgical treatments that restore normal knee function. The purpose of this study was to develop a technique that enables parametric analysis of the effect of the ACL reconstruction (ACLR) in cadaver knees, by replacing its contributions with that of a specimen-specific virtual ACLR that can be enabled, disabled, or modified. Twelve ACLR reconstructed knees were mounted onto a motion simulator. In situ ACLR graft forces were measured using superposition, and these data were used to design specimen-specific virtual ACLRs that would yield the same ligament force-elongation behaviors. Tests were then repeated using the virtual ACLR in place of the real ACLR and following that in ACL deficient knee by disabling the virtual ACLR. In comparison to the ACL deficient state, the virtual ACLRs were able to restore knee stability to the same extent as real ACLRs. The average differences between the anterior tibial translation (ATT) of the virtual ACLR versus the real ACLR were +1.6 ± 0.9 mm (p = 0.4), +2.1 ± 0.4 mm (p = 0.4), and +1.0 ± 0.9 mm (p = 0.4) during Anterior drawer, Lachman and Pivot-shift tests, respectively, which is small in comparison to the full ATT range of motion (ROM) of these knees. Therefore, we conclude that a virtual ACLR can be used in place of real ACLR during biomechanical testing of cadaveric knees. This capability opens the door for future studies that can leverage parameterization of the ACLR for surgical design optimization.
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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 | 0.001 |
| 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".