Outcomes of Arthroscopic Double-bundle PCL Reconstruction Using the LARS Artificial Ligament
Bibliographic record
Abstract
The purpose of this study was to assess the outcomes of posterior cruciate ligament (PCL) reconstruction using the Ligament Augmentation and Reconstruction System (LARS) (JK Orthomedic Ltd, Dollard-des-Ormeaux, Quebec, Canada) artificial ligament. Compared with older artificial ligaments, the LARS, which has been used in Europe for 15 years, is more resistant to wear and tear, has satisfactory torsional fatigue resistance, and has good biocompatibility. The current is study included 38 double-bundle PCL reconstructions using the LARS artificial ligament in 38 patients. Mean patient age was 32.6 years, and mean time from injury to surgery was 6 months. Mean follow-up was 37 months (range, 30-68 months). The study endpoint was 2-year follow-up. Mean Tegner score improved from 3.4 ± 0.6 preoperatively to 6.0 ± 1.4 postoperatively (P<.001), and mean Lysholm score improved from 70.0 ± 11.0 pre-operatively to 91.7 ± 5.5 postoperatively (P<.001). Knee laxity decreased significantly postoperatively (P<.001), and no differences existed at 1- and 2-year follow-up. After surgery using the Y-type LARS artificial ligament, knee function and stability improved. Using the LARS artificial ligament for double-bundle reconstruction of the PCL avoids donor-site morbidity and disease transmission. The complication rate is low, and the results appear to be stable with time and comparable with those obtained with other grafts. Double-bundle PCL reconstruction with the LARS artificial ligament may be an alternative treatment option.
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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) | 0.001 | 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) | 0.001 | 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".