All-epiphyseal anterior cruciate ligament reconstruction produces good functional outcomes and low complication rates in pediatric patients: a systematic review
Bibliographic record
Abstract
PURPOSE: To assess the literature on indications, outcomes, and complications in pediatric patients undergoing all-epiphyseal (AE) anterior cruciate ligament reconstruction (ACLR). METHODS: PubMed, Medline, and Embase were searched for literature evaluating AE ACLR in pediatric patients. All included studies were assessed for quality using the Methodological Index for Non-Randomized Studies (MINORS). Descriptive statistics are presented where applicable. RESULTS: Overall, 17 studies comprising 545 patients, with a mean age of 12.0 ± 1.2 (range 8-19) met the inclusion criteria. The graft choices in this systematic review included hamstring tendon autografts (75.4%, n = 403), quadriceps tendon autograft (6.2%, n = 33), Achilles tendon allograft (3.6%, n = 19) and posterior tibialis tendon allograft in one patient (0.2%, n = 1). Time of return-to-sport ranged from 8 to 22 months. Postoperative subjective IKDC scores were above 90 points. The rate of return-to-sport after AE ACLR was 93.2% (n = 219/235) and 77.9% (n = 142/183) of patients returned to sport at pre-injury level. The overall complication rate was 9.8% (n = 53/545) with the most common complication being ACL re-rupture (5.0%; n = 27/545). Only 1.5% (n = 8/545) of patients demonstrated growth disturbances. CONCLUSION: Overall, the AE ACLR technique can achieve good postoperative functional outcomes while notably minimizing the incidence of primary issue of physeal disruption and potential associated leg-length discrepancies. AE ACLR should be considered in pediatric patients with at least 2 years of skeletal growth remaining based on radiographic bone age to minimize the impact of growth-related complications. LEVEL OF EVIDENCE: IV (Systematic Review of Level III and IV evidence).
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".