All-Epiphyseal Versus Micheli-Kocher Anterior Cruciate Ligament Reconstruction in Skeletally Immature Patients: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Early physeal-sparing anterior cruciate ligament reconstruction (ACLR) is considered the optimal treatment method in the skeletally immature population to preserve the integrity of the knee joint while reducing the risk of growth disturbances and angular deformities. Contemporary treatment algorithms recommend the use of all-epiphyseal (AE) or Micheli-Kocher (MK) ACLR techniques in patients with considerable growth remaining. Nevertheless, no research exists comparing the 2 techniques. Therefore, the purpose of this review is to comprehensively compare postoperative outcomes and complication profiles following AE and MK ACLR in skeletally immature patients. METHODS: A systematic search of Embase, Medline, and PubMed was conducted from inception to April 30, 2024. All studies reporting outcomes and/or complications following AE or MK ACLR were included. Screening and data abstraction were designed in accordance with PRISMA and R-AMSTAR guidelines. RESULTS: Twenty-nine studies with 1177 patients were included. AE ACLR and MK ACLR yielded similar results for rates of return to preinjury level of activity (91.8% and 93.4%, respectively), negative pivot-shift (93.9% and 95.2%, respectively) and Lachman test grades (93.9% and 90.8%, respectively), IKDC subjective scores (94.0 and 93.6, respectively), ROM flexion (144.1 degrees and 136.3 degrees, respectively) and hyperextension (2.5 degrees and 3.1 degrees, respectively). AE ACLR yielded a greater risk of growth disturbances, angular deformities, and graft failures (1.5%, 1.3%, and 10.6%, respectively) but a lower risk of contralateral ACL tears (4.2%) relative to MK ACLR (0.0%, 0.0%, 6.6%, and 6.6%, respectively). CONCLUSIONS: Both AE and MK ACLR yield promising rates of RTS, substantially limit anteroposterior laxity, surpass IKDC thresholds for substantial clinical benefit, and regain fully functional ROM to comparable levels, though they yield marginally different complication profiles. However, the majority of the included studies were moderate-quality or low-quality evidence with high statistical heterogeneity. Therefore, no statistical conclusions regarding the differences in complication profiles can be drawn. Future randomized controlled trials or large prospective cohort studies should compare the efficacy and complication profile of QT autograft AE ACLR relative to MK ACLR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".