Lateral extra-articular procedures in anterior cruciate ligament reconstruction: Narrative review on current evidence
Bibliographic record
Abstract
Techniques for anterior cruciate ligament reconstruction (ACLR) have evolved over time, but residual anterolateral rotatory instability (ALRI) often persists, which can result in poor patient-reported outcomes and an increased rate of graft failure. The re-emergence of lateral extra-articular procedures (LEAPs), either lateral extra-articular tenodesis (LET) or the newer anterolateral ligament reconstruction, in combination with ACLR has changed practice significantly. Here we describe a literature review of ACLR and LEAPs, focusing on LET. Studies describing anatomy, history, biomechanical and clinical studies, and current practice regarding ACLR and LET were searched in PubMed. Our current practice and future directions are also discussed. There is accumulating evidence supporting the effectiveness of LEAP in controlling ALRI and reducing the risk of anterior cruciate ligament (ACL) graft failure. A number of cadaveric studies have shown that LEAP would help control ALRI in combination with ACLR. There are also several randomized controlled studies showing that addition of LET significantly reduces the risk of ACL graft failure. However, only a paucity of data about its long-term outcomes are currently available. LEAP has had a great impact on our daily practice. However, indications, optimal ACL graft type, and long-term outcomes still need to be thoroughly investigated. • Despite advancement in orthopedics, anterior cruciate ligament reconstruction (ACLR) still has a significant graft failure rate. • There is growing attention to perform lateral extra-articular tenodesis (LET) in addition to ACLR to control rotational laxity. • Many biomechanical and clinical studies have shown improved rotational stability if ACLR is combined with LET. • Further data are needed to determine the indication and long-term outcomes after ACLR with LET.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".