A new generation of artificial ligaments in reconstruction of the anterior cruciate ligament: Two-year follow-up of a randomised trial
Bibliographic record
Abstract
We have undertaken a randomised clinical trial comparing two methods of reconstruction of the anterior cruciate ligament in patients with chronic instability. We used an ipsilateral bone-patellar-tendon-bone autograft in 27 patients and the Ligament Advancement Reinforcement System (LARS) artificial ligament in 26. Assessment before and at two, six, 12 and 24 months after surgery, included the history, physical examination, a modified International Knee Documentation Committee (IKDC) score, the Tegner score, the Knee Injury and Osteoarthritis Outcome Score (KOOS) and instrumented laxity testing. There were no cases of reactive synovitis or of infection of the knee, and there was no difference regarding the failure rate between the two groups. The IKDC showed no significant differences between the two groups at any stage of the follow-up. The KOOS evaluation showed consistently better results in all subscales for the LARS group during the first year of follow-up. After 24 months these differences were no longer evident. Instrument-tested laxity was greater in the LARS group at all stages of follow-up, but the differences were not significant at 24 months. Our findings suggest that at follow-up at 24 months the LARS ligament seems to be a satisfactory treatment option, especially when an early return to high levels of activity is demanded.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| 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".