Functional outcomes of simultaneous ACL reconstruction and LET using all-suture anchor – a modified mini-open technique
Bibliographic record
Abstract
INTRODUCTION: The anterior cruciate ligament (ACL) rupture frequently leads to instability of the knee joint, which subsequently damages other intra‑articular structures. The combination of ACL reconstruction (ACLR) with concurrent lateral extra‑articular tenodesis (LET) improves rotational stability and reduces the risk of subsequent ACL rupture. However, there is not much research that specifically outlines LET hardware and surgical methods. AIM: This study aimed to describe and evaluate clinical outcomes of a mini‑open modified Lemaire technique using a self‑punching all‑suture anchor. MATERIALS AND METHODS: In this study, 32 patients underwent primary or revision ACLR combined with LET via the mini‑open modified Lemaire technique using a self‑punching all‑suture anchor. All individuals completed the following pre‑ and postoperative questionnaires to evaluate their functional performance: the Knee Injury and Osteoarthritis Outcome Score, assessing several domains, the International Knee Documentation Committee subjective knee evaluation form, the Lysholm knee scoring scale, and the Western Ontario and McMaster Universities Arthritis Index. Complication rates were also assessed. RESULTS: Each patient's functional score values increased, as compared with preoperative measure‑ ments. There were no early post‑ or intraoperative complications associated with the technique described. CONCLUSIONS: This is the first study that evaluated clinical outcomes, intraoperative, and early post‑ operative complications of the mini‑open modified Lemaire technique using a self‑punching all‑suture anchor. Our study indicates that this procedure is effective, safe, and associated with better cosmesis than classic LET techniques.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".