Association of COL5A1 gene polymorphisms and risk of tendon-ligament injuries among Caucasians: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Tendons and ligaments are common sites of musculoskeletal injuries especially during physical activity. The multifactorial etiology of tendon-ligament injury (TLI) includes both genetic and environmental factors. The genetic component could render influence on TLI risk to be either elevation or reduction. OBJECTIVE: Inconsistency of reported associations of the collagen type V alpha 1 chain (COL5A1) polymorphisms, mainly rs12722 (BstUI) and rs13946 (DpnII), with TLI warrant a meta-analysis to determine more precise pooled associations. METHODS: Multi-database literature search yielded eight articles (11 studies) for inclusion. Pooled odds ratios (ORs) and 95% confidence intervals were used to estimate associations. Heterogeneity of outcomes warranted examining their sources with outlier treatment. RESULTS: = 0%) with outlier treatment. Significant associations (ORs 0.26-0.65, p = 0.002-0.03) were also observed in other COL5A1 polymorphisms (rs71746744 and rs16399). Sensitivity analysis deemed all significant outcomes to be robust. CONCLUSIONS: In summary, COL5A1 polymorphisms reduce the risk of TLI among Caucasians. These findings are based on the evidence of significance, homogeneity, consistency, and robustness. Additional studies are warranted to draw more comprehensive conclusions.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.022 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".