What is the best evidence for graft choice in ACL reconstruction? Protocol for a systematic review and network meta-analysis
Bibliographic record
Abstract
ABSTRACT Introduction Anterior cruciate ligament (ACL) reconstruction is one of the most commonly performed sports medicine procedures. A variety of grafts are currently used for reconstruction, including both allograft and autograft. Despite numerous meta-analyses, there exists no high-quality quantitative synthesis of all randomized controlled trial (RCT) data on graft choice. Objective To identify the optimal graft choice for ACL reconstruction by performing the first systematic review and network meta-analysis (NMA) to include both functional outcomes and complications. Methods Multiple digital databases including MEDLINE, Embase, and CENTRAL will be searched independently and in duplicate for RCTs randomizing graft choice in ACL reconstruction in skeletally mature patients. A Bayesian framework with a random-effects model will be used for NMA. Surface under the cumulative ranking curve (SUCRA) values will be used to generate a rank list for each outcome. Results will be reported as mean differences (MD) (or standardized mean difference, if necessary) or relative risk (RR) with 95% credible intervals (CI). Comparisons will be inferred to be statistically significant if the 95% CI of MD does not cross zero or if the 95% CI of relative risk does not cross one. Studies will be assessed for quality using the Cochrane risk of bias assessment tool. Quality of evidence for each network comparison will be determined as per the Grades of Recommendation, Assessment, Development and Evaluation (GRADE) approach for network meta-analyses. This NMA will be reported according to the PRISMA extension statement for network meta-analyses Outcomes of interest Functional outcomes of interest including range of motion, return to activity/sport, and IKDC, Lysholm, Tegner, ACL-QOL, and KOOS scores. Persistent laxity as measured by Lachman, Pivot-shift, side-to-side, and measured laxity (e.g. KT-1000) will also be analyzed. Complications (e.g. infection, graft failure, donor site pain), tunnel osteolysis, and failure (including but not limited to graft rupture and/or persistent laxity) will be compared between grafts. Relevance/Impact This NMA will be the first high-quality syntheses of all randomized evidence regarding graft choice in ACL reconstruction. As the first analysis to compare all major graft choices independently, it will be used to inform surgeon-patient decision making. It has the reasonable possibility of changing clinical practice.
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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 | 0.069 | 0.144 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.033 | 0.039 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.058 | 0.005 |
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".