COL5A1 rs13946 Polymorphism and Anterior Cruciate Ligament Injury: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Anterior cruciate ligament (ACL) injury (ACLI) is a prevalent sports injury. Genetic factors play a crucial role in determining the risk of ACLI. This systematic review aimed to identify the association between the COL5A1 rs13946 polymorphism and susceptibility to ACLI. Methods: Searches were performed in PubMed Central, Web of Science, EBSCOhost, Scopus, and CNKI. The Newcastle–Ottawa Scale (NOS) was used to assess potential bias, and data from the included studies were analyzed using RevMan 5.4. The odds ratio (OR) and 95% confidence intervals (95% CI) were calculated to determine the strength of the association between COL5A1 rs13946 and the risk of anterior cruciate ligament injury. A p value < 0.05 was considered statistically significant. Seven studies met the inclusion criteria for screening the association between COL5A1 rs13946 and ACL injury and were included in this meta-analysis. The meta-analysis revealed no significant heterogeneity across five genetic models. Statistically significant findings were observed in the recessive (OR = 1.29, 95% CI [1.06, 1.58], p = 0.01) and allele models (OR = 0.85, 95% CI [0.73, 1.00], p = 0.04). The TT genotype or T allele of rs13946 showed a distinct susceptibility to ACLI under the recessive model, particularly in Caucasians. This study supports the association between COL5A1 rs13946 and the risk of ACLI, particularly in Caucasians. More specifically, the C/- genotype of rs13946 provides protection against ACLI in Caucasians. Further research with larger sample sizes and well-balanced gender-specific cohorts is necessary to validate this association and strengthen our findings.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".