Clinical Outcomes After Ipsilateral Versus Contralateral Autograft Harvest for Anterior Cruciate Ligament Reconstruction: A Systematic Review
Bibliographic record
Abstract
Background: Contralateral donor autografts in anterior cruciate ligament (ACL) reconstruction (ACLR) may act as an alternative to conventional ipsilateral donor grafts but are rarely used clinically because of the lack of evidence on patient outcomes and concerns around additional morbidity. Purpose: To investigate the effect of contralateral versus ipsilateral autograft use in ACLR on patient outcomes. Study Design: Systematic review; Level of evidence, 4. Methods: The MEDLINE, Embase, and Cochrane Central Register of Controlled Trials databases were searched from inception to October 2022 for comparative studies assessing the clinical or functional outcomes of ipsilateral versus contralateral autograft harvest in primary or revision ACLR. Given the heterogeneity of the included studies, data were summarized using descriptive statistics. Results: Included were 11 studies representing 1638 patients with a mean follow-up of 49 months. The mean time to return to sport was shorter in patients treated with a contralateral bone-patellar tendon-bone (BPTB) autograft in 2 of 3 studies that evaluated this outcome after primary ACLR and in the only study that evaluated this outcome after revision ACLR. Some studies found improved strength recovery in the contralateral ACL-reconstructed knee. Otherwise, there was no significant difference between contralateral and ipsilateral ACLRs on subjective or objective postoperative clinical outcome scores. Most studies reported minimal donor site morbidity. Clinical adverse events including postoperative graft rerupture and infection were low in both contralateral and ipsilateral ACLRs and were not significantly different. Conclusion: Contralateral ACL autograft harvest may lead to earlier return to sport when patients undergo BPTB ACLR. However, clinical outcomes, morbidity, risk of rerupture, and risk of donor knee injury were not significantly different in this review.
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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) | 0.009 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".