Biological augmentation of anterior cruciate ligament reconstruction with bone marrow aspirate concentrate: a systematic review and meta-analysis of randomised controlled trials
Bibliographic record
Abstract
Abstract Purpose Biological augmentation of anterior cruciate ligament (ACL) reconstruction with bone marrow aspirate concentrate (BMAC) is gaining attention for its theoretical potential to enhance postoperative healing and recovery. However, its clinical benefits remain uncertain, and its high cost raises questions about efficacy. Hence, we systematically reviewed randomised controlled trials (RCTs) to evaluate the effectiveness of BMAC in ACL reconstruction. Methods Our search included Cochrane, EMBASE, OVID, PubMed, and Scopus databases for RCTs evaluating the use of BMAC in ACL reconstruction. Primary outcomes focused on International Knee Documentation Committee (IKDC) scores and Lysholm scores. Secondary outcomes included MRI-related outcomes and postoperative complications. Statistical analysis was conducted using Review Manager 5.4 (Cochrane Collaboration), with heterogeneity assessed using Cochrane’s Q test and I2 statistics. Results 221 patients from five RCTs were included, with 109 (49.3%) receiving BMAC augmentation. Follow-up ranged from 11.05 to 24 months. No significant differences were found in postoperative IKDC scores between the BMAC and control groups at, three, six and 12 months. The BMAC group had significantly higher IKDC scores at 24 months; however, this difference was unlikely to be clinically significant. No significant differences were observed in postoperative Lysholm scores at 12 or 24 months. MRI-related outcomes suggested potential graft recovery improvement with BMAC, and complication rates were comparable between groups. Conclusion In summary, biological augmentation with BMAC in ACL reconstruction does not significantly improve early patient-reported outcomes but offers potential benefits in graft recovery without increasing complication rates.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.027 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".