LARS synthetic ligaments for the acute management of 111 acute knee dislocations: effective surgical treatment for most ligaments
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to describe the longitudinal outcomes of acute repair and augmentation for the reconstruction of dislocated knees, using LARS synthetic ligaments. METHODS: Patients with a knee dislocation surgically treated using LARS synthetic ligament augmentation, with a minimum follow-up of 24 months, were enrolled between 1996 and 2014. Range of motion, Lachman, pivot shift, posterior drawer, step off sign, valgus, varus, KT-1000 arthrometer, Telos technique, IKDC, Lysholm, Tegner, and Meyers scores were obtained every 2 years up to 10 years. RESULTS: Median age was 32.1 years (IQR 23.2-43.3) at time of surgery. Median time from trauma to surgery was 9 days and mean follow-up time was 6.6 years. Median questionnaire scores were: Lysholm 79.5 (IQR 65.0-89.0), Tegner 4.0 (IQR 3.7-6.0), Meyers 3.0 (IQR 3.0-4.0), and mean IKDC was 63.8 (SD 18.9). Median flexion and extension of the injured knee was 124° (IQR 115-129.5) and 0° (IQR - 5 to 0), respectively. Median KT-1000 differential was 0.7 mm (IQR 0.1-3.1) for ACL and 0.9 mm (IQR 0.2-1.4) for PCL. Mean differential for Telos was 2.5 mm (SD 5.8) for ACL, 4 mm (IQR 2-6.3) for PCL 30°, and 8.2 mm (SD 4.4) for PCL 90° (consistent with PCL laxity). More than 90% of patients had good anterior articular stability and > 60% of patients had good posterior articular stability. CONCLUSIONS: Acute repair and augmentation of knee dislocations with LARS synthetic ligaments resulted in satisfactory outcomes for the ACL and collateral structures. Telos stress radiography showed PCL laxity in more than half of cases despite low laxity results with KT-1000. The perception of patients about knee function was sustained in time. LEVEL OF EVIDENCE: IV.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".