Association of COL5A1 gene polymorphisms and musculoskeletal soft tissue injuries: a meta-analysis based on 21 observational studies
Bibliographic record
Abstract
OBJECTIVE: Inconsistent findings existed on the correlation of collagen type V α1 (COL5A1) gene polymorphisms and musculoskeletal soft tissue injuries (MSTIs). The purpose of this study was to collect and combine the current evidences by a meta-analysis approach. METHODS: Six online databases were searched up to August, 2021. The methodological quality of each individual study was evaluated based upon Newcastle-Ottawa Scale (NOS). The strength of the effect size was presented by odds ratio (OR) with 95% confidence interval (95%CI) in five genetic models. The data were analyzed using Review Manager 5.3. RESULTS: Twenty-one studies were eligible to this meta-analysis. The study quality was deemed fair to excellent according to NOS. In the overall analyses, the merged data suggested that rs12722, rs71746744, and rs3196378 polymorphisms were correlated to an increased susceptibility to MSTIs. But the association was not established in rs13946 or rs11103544 polymorphism. For rs12722 polymorphism, stratified analyses by injury type and ethnicity identified the association mainly existed in ligament injury and among Caucasian population. For rs13946 polymorphism, subgroup analysis suggested the association existed in tendon and ligament injuries. CONCLUSION: This study supports that rs12722 is associated with an elevated susceptibility to ligament injury, especially in the Caucasian population. Rs13946 polymorphism appears to increase the risk to tendon and ligament injuries. Rs71746744 and rs3196378 polymorphisms have a tendency to confer an elevated risk to MSTIs. However, no relevance is found between rs11103544 polymorphism and MSTIs.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.038 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".