All‐Inside Anterior Cruciate Ligament Reconstruction Had Clinical Outcome Similar to the Transtibial Technique Except for Improved Side‐to‐Side Difference and Tegner Activity Scale: A Systematic Review and Meta‐analysis
Bibliographic record
Abstract
PURPOSE: To compare clinical outcomes of the all-inside technique with the transtibial technique in anterior cruciate ligament reconstruction based on available literature on this topic. METHODS: According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist, we conducted a systematic search for randomized controlled trials and cohort studies. Our comprehensive search encompassed PubMed, Embase, Cochrane Library, and Web of Science. We included randomized controlled trials (RCTs) and cohort studies that compared the 2 techniques with a minimal 1-year follow-up. Two independent authors assessed RCTs using the risk of bias tool developed by the Cochrane Collaboration and evaluated the quality of cohort studies using the Newcastle-Ottawa Scale for Assessing the Quality of Nonrandomized Comparative Trials. The subjective and objective outcomes, complications, and graft failure were obtained. R software was used to perform the analysis. RESULTS: The present analysis enrolled 9 RCTs (n = 687) and 11 cohort studies (n = 910). After a minimal 1-year follow-up in RCTs, functional outcomes such as International Knee Documentation Committee (IKDC) subjective score, Lysholm score, Tegner activity scale, Knee Society Score, and hop test were found to be similar between 2 techniques. The laxity outcomes, including the IKDC objective grade and pivot-shift test, were suggested to be comparable. There was a significant difference favoring the transtibial technique in terms of side-to-side difference (P = .04; 95% confidence interval [CI], 0.08-0.90). The pooled data from cohort studies indicated equivalent results in terms of IKDC subjective score, Lysholm score, side-to-side difference, IKDC objective grade, complications, and graft failure, with the exception of statistical difference in the Tegner activity scale (P = .03; 95% CI, -0.50 to -0.04). CONCLUSIONS: Our findings suggest that there is no difference in most outcome scores between the all-inside and transtibial techniques for anterior cruciate ligament reconstruction. There are statistically significant differences in side-to-side difference and Tegner activity scale favoring the all-inside technique. LEVEL OF EVIDENCE: Level IV, meta-analysis of Level I to IV studies.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".