COL12A1 rs970547 Polymorphism Does Not Alter Susceptibility to Anterior Cruciate Ligament Rupture: A Meta-Analysis
Bibliographic record
Abstract
Objective: Currently available evidence regarding the association between collagen type XII α1 (COL12A1) polymorphism and risk of anterior cruciate ligament rupture (ACLR) remains elusive. The aim of our present study was to assess the association between COL12A1 rs970547 polymorphism and ACLR risk. Methods: Five online databases, namely, PubMed, EMBASE, ISI Web of Science, CENTRAL, and CNKI, were searched from their inception data up to December 2020 to identify relative observational studies. The methodological quality of each individual study was evaluated using the Newcastle-Ottawa Scale (NOS). The “model-free approach” was employed to estimate the magnitude of effect of COL12A1 rs970547 polymorphism on ACLR, and the association was expressed using odds ratio (OR) and its associated 95% confidence interval (95% CI). Subgroup analysis was performed by ethnicity and sex of included subjects. Results: Eight studies involving 1,477 subjects with ACLR and 100,439 healthy controls were finally included in our study. The methodological quality of included studies was deemed moderate to high based on NOS scores. The “model-free” approach suggested no genotype differences between ACLR and healthy control for the rs970547 polymorphism, but we still used the allele model to present the combined data. Under the random-effect model, there was no significant difference in the frequency of effecting allele between ACLR and control (OR: 0.91, 95% CI 0.77, 1.08; p = 0.28). Stratified analysis by sex and ethnicity also showed no difference in allele frequency. Conclusion: The findings of this current meta-analysis suggested that rs970547 was not associated with ACLR risk in male, female, and the overall population among Asians or Caucasians.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.049 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".