Lateral Augmentation Procedures in Anterior Cruciate Ligament Reconstruction: Anatomic, Biomechanical, Imaging, and Clinical Evidence
Bibliographic record
Abstract
BACKGROUND: There has been an increasing interest in lateral-based soft tissue reconstructive techniques as augments to anterior cruciate ligament reconstruction (ACLR). The objective of these procedures is to minimize anterolateral rotational instability of the knee after surgery. Despite the relatively rapid increase in surgical application of these techniques, many clinical questions remain. PURPOSE: To provide a comprehensive update on the current state of these lateral-based augmentation procedures by reviewing the origins of the surgical techniques, the biomechanical data to support their use, and the clinical results to date. STUDY DESIGN: Systematic review. METHODS: A systematic search of the literature was conducted via the Medline, EMBASE, Scopus, SportDiscus, and CINAHL databases. The search was designed to encompass the literature on lateral extra-articular tenodesis (LET) procedures and the anterolateral ligament (ALL) reconstruction. Titles and abstracts were reviewed for relevance and sorted into the following categories: anatomy, biomechanics, imaging/diagnostics, surgical techniques, and clinical outcomes. RESULTS: The search identified 4016 articles. After review for relevance, 31, 53, 27, 35, 45, and 78 articles described the anatomy, biomechanics, imaging/diagnostics, surgical techniques, and clinical outcomes of either LET procedures or the ALL reconstruction, respectively. A multitude of investigations were available, revealing controversy in addition to consensus in several categories. The level of evidence obtained from this search was not adequate for systematic review or meta-analysis; thus, a current concepts review of the anatomy, biomechanics, imaging, surgical techniques, and clinical outcomes was performed. CONCLUSION: Histologically, the ALL appears to be a distinct structure that can be identified with advanced imaging techniques. Biomechanical evidence suggests that the anterolateral structures of the knee, including the ALL, contribute to minimizing anterolateral rotational instability. Cadaveric studies of combined ACLR-LET procedures demonstrated overconstraint of the knee; however, these findings have yet to be reproduced in the clinical literature. The current indications for LET augmentation in the setting of ACLR and the effect on knee kinematic and joint preservation should be the subject of future research.
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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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".