Genetic Determinants of the Anterior Cruciate Ligament Rupturein Sport: An Up-to-Date Systematic Review
Bibliographic record
Abstract
Anterior cruciate ligament injuries (ACLIs) are one of the most common knee injuries in sports. Although numerous factors have been related to the risk of ACLIs, it is still unclear why some individuals are more susceptible than others due to the intricate etiology of ACLIs. Several genetic factors have been identified as contributing to ACLIs. This systematic review summarizes the current evidence regarding the genetic causes of ACLIs based on the available literature. Five electronic databases were searched from 2017 to 2022. All titles, abstracts, and full texts were reviewed in detail to determine the inclusions and exclusions. The Newcastle-Ottawa Scale was used to evaluate the risk of bias. The studies' characteristics and results are presented in both narrative and tabular formats. A total of 24 studies examined 31 genes and 62 variants associated with ACLIs in the global population. Ten studies investigated seven collagens and ten SNPs for the ACL injury. The majority of studies found no significant difference in the association of the COL1A1 rs1800012, COL5A1 rs12722, VEGFA rs1570360, IL6R rs2228145, IL6 rs1800795, IL1B rs16944 and rs1143627, however, contrary results were found when nationality and gender were considered together. Conflicting evidence was found for polymorphisms rs2010963, rs699947 of the VEGFA gene in different studies. Due to a lack of data, it was impossible to determine the relationship between the anterior cruciate ligament rupture (ACLR) and the other polymorphisms. More research is required to establish a clear relationship between the ACLR and genetic variants, particularly when gender and nationality are taken into account separately.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.010 |
| 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) | 0.007 | 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".