Anatomical anterior cruciate ligament reconstruction (ACLR) results in fewer rates of atraumatic graft rupture, and higher rates of rotatory knee stability: a meta-analysis
Bibliographic record
Abstract
Importance This review highlights the differences in outcomes between anatomical and non-anatomical anterior cruciate ligament reconstruction (ACLR) techniques. Objective To compare clinical and functional outcomes between anatomical and non-anatomical ACLR techniques. Evidence review A search of MEDLINE, Embase and PubMed from 1 January 2000 to 24 October 2019 was conducted. Randomised and prospective primary ACLR studies using autograft and a minimum of 2 years of follow-up were included. The Anatomic Anterior Cruciate Ligament Reconstruction Checklist (AARSC) was used to categorise studies as anatomical. Outcomes analysed included failure rate, knee stability and functional outcomes. A meta-analysis using risk ratio and mean differences was conducted using a random effects model. Findings Thirty-six studies were included, representing 3710 patients with a follow-up range of 24–300 months. The overall failure rate was 96/1470 (6.5%) and 131/1952 (6.7%) in the anatomical group and non-anatomical group, respectively. The pooled results of the overall failure rate showed that there was no statistically significant difference between the anatomical and the non-anatomical groups (p=0.96). There were 37/60 (61.7%) and 29/67 (43.3%) traumatic failures in the anatomical and non-anatomical groups, respectively. The number of patients with the negative postoperative pivot-shift test was 995/1252 (79.5%) and 1140/1589 (71.1%) in the anatomical and non-anatomical groups, respectively. The pooled results indicated a statistically significant higher number of patients with a positive pivot shift in the non-anatomical group compared with the anatomical group (p=0.03). Conclusions and relevance This study demonstrated that the overall failure rate was similar between the anatomical and non-anatomical approaches. However, the anatomical ACLR demonstrated a significantly superior restoration of rotatory stability, as evidenced by a higher percentage with a negative postoperative pivot-shift test. Non-anatomical ACLR resulted in higher rates of atraumatic graft ruptures and persistent rotatory knee instability. Surgeons should consider anatomical ACLR when treating rotatory knee stability in patients. Level of evidence II, systematic review and meta-analysis of level I and II 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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.030 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".