All-Inside Anterior Cruciate Ligament Reconstruction—A Systematic Review of Techniques, Outcomes, and Complications
Bibliographic record
Abstract
The all-inside technique (AIT) for anterior cruciate ligament reconstruction (ACLR) is gaining popularity as a more anatomic, less invasive, technique with the potential for more rapid recovery. This systematic review aims to critically assess components of the technique, its safety profile, outcomes, and complications. PUBMED, EMBASE, and MEDLINE were searched for studies discussing primary ACLR using the AIT. Article screening, quality assessment, and data abstraction were completed in duplicate, and a minimal clinically important difference (MCID) was used to frame the descriptive results in a clinically significant context. A total of 13 studies satisfied the inclusion criteria. Five-hundred twenty six patients (mean age 31.9 ± 5.9 years) underwent ACLR using the AIT. The mean follow-up was 18.2 ± 7.7 months. Graft choice included autograft (73.8%) and allograft (26.2%). Drilling techniques for femoral sockets were outside-in (53.4%) and through the anteromedial portal (35.4%), whereas tibial sockets were drilled outside-in (35.0%) and through the superomedial portal (3.8%). The rehabilitation protocol had an immediate focus on obtaining full knee extension, jogging permitted 2 to 4 months postoperatively in 61.5% of studies, and return to cutting and pivoting sports 6 to 9 months postoperatively in 69.2% of studies. A MCID was reached for subjective International Knee Documentation Committee scores at 6, 12, and 24 months follow-up and Lysholm knee score at 24 months follow-up. An improvement in outcomes was most notably between 6 and 12 months postoperatively. There was a total of 31 complications (5.89%) and included graft rerupture (2.47%), loss of extension of 1° to 10° (1.14%), and cartilage or meniscus injuries on the operated knee (0.760%). Complications related to the surgical technique were not reported. The AIT for ACLR shows potential as a minimally invasive approach given the low graft failure rates and short-term improvements in knee function and stability, pain and patient important outcomes from this approach. Comparative studies with large sample sizes and a long-term follow-up are required to assess the proposed advantages of this technique. This is a Level IV study.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".