Application of internal brace ligament augmentation technique in knee ligament injury: a systematic review
Bibliographic record
Abstract
BACKGROUND: Anterior cruciate ligament (ACL) injuries are common and can lead to significant knee instability and an increased risk of long-term cartilage damage. Given the emerging role of internal brace ligament augmentation (IBLA) in treating these injuries, this systematic review aimed to evaluate the collective evidence on the safety and effectiveness of IBLA in patients with anterior cruciate ligament injury. METHODS: We systematically searched the PubMed, Embase, Cochrane Library, and Web of Science databases until July 2025. The literature was screened according to the inclusion and exclusion criteria, and data were extracted. The extracted key data included the International Knee Documentation Committee score (IKDC), Tegner score, Lysholm score, Knee Injury and Osteoarthritis Outcome Score (KOOS), Western Ontario and McMaster Universities Arthritis Index score (WOMAC), Marx Activity Scale, visual analogue scale (VAS), and the Veterans RAND 12-Item Health Survey (VR-12). The quality of nonrandomized trials was assessed using the Newcastle-Ottawa Scale (NOS). RESULTS: Systematic screening identified 11 studies (n = 676 patients) for analysis. Patient-reported outcomes demonstrated significant improvements post-intervention. Meta-analyses demonstrated statistically significant increases in KOOS (MD = 36.86, 95% CI: 32.51-41.20, p < 0.01), VR-12 (MD = 16.62, 95% CI:14.75-18.49, p < 0.01), and decreases in visual analog scale (VAS) (MD = -2.82, 95% CI: -3.40 to -2.25, p < 0.01). Lysholm (postoperative 89-94) and IKDC scores (postoperative 85-91) approached or exceeded pre-injury levels. Tegner scores remained stable near pre-injury levels (5.33-6.4). Marx activity scores showed a significant decrease (MD = -3.84, 95% CI: -6.19 to -1.49, p < 0.01), potentially indicating postoperative activity adaptation. Study heterogeneity was noted. All included studies demonstrated mild to high quality. CONCLUSIONS: IBLA appears to be a promising technique for improving functionality, stability, and pain management in anterior cruciate ligament injury. However, the current evidence is significantly constrained by small sample sizes, a predominance of low-quality studies, and a lack of long-term comparative data. Therefore, further rigorous, high-quality research is required to definitively establish the safety and long-term effectiveness of IBLA. LEVEL OF EVIDENCE: III.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".