Acute patellar ligament reconstruction with the synthetic LARS ligament as an internal stabilizer
Bibliographic record
Abstract
Background: Acute patellar ligament ruptures present significant surgical challenges due to the essential role of the knee extensor apparatus in gait and postural stability. While direct repair is standard, compromised tissue quality from chronic tendinosis or high functional requirements in athletes may necessitate augmented reconstruction to prevent failure and allow accelerated rehabilitation. Objective: This article describes a surgical technique for reconstructing the patellar ligament using longitudinal synthetic polyethylene terephthalate (PET) fibers as an internal stabilizer for both acute and chronic injuries. Key Points: The technique utilizes two LARS PTR 30 ligaments, each providing a tensile strength of 1500 N with 9% elasticity. The procedure involves creating two parallel 4.5 mm longitudinal tunnels in the patella and two tunnels at the tibial tubercle. The synthetic ligaments are anchored proximally via stainless-steel barrettes at the superior patellar pole and secured distally in the tibia using 5.2 x 30 mm metal interference screws. This internal bracing allows for end-to-end suture of the native ligament stumps under reduced tension. Postoperative management includes immediate weightbearing in an extension brace, followed by progressive range-of-motion exercises starting at two weeks. Advantages include the absence of donor site morbidity, high resistance to plastic deformation, and histological evidence of fibroblast ingrowth into the PET mesh. Conclusion: Internal stabilization with synthetic PET ligaments provides a high-strength alternative for patellar tendon reconstruction. This approach facilitates early mobilization and rapid return to activity, particularly in complex cases or high-demand patients where native tissue quality is suboptimal.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".