The association between rs12901499 polymorphism in SMAD3 gene and risk of osteoarthritis: a meta-analysis
Bibliographic record
Abstract
Shu-Tao Gao,1 Zheng-Tao Lv,2 Wei-Bin Sheng1 1Department of Spine Surgery, The First Affiliate Hospital, Xinjiang Medical University, Urumqi, Xinjiang, China; 2Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China Purpose: This study was conducted to assess and synthesize the current evidence on the association between rs12901499 polymorphism in SMAD3 gene and risk of osteoarthritis (OA). Materials and methods: Four electronic databases, including PubMed, Embase, ISI Web of Science, and CENTRAL were systematically searched for potential studies. Summary odds ratio and corresponding 95% CI were calculated to evaluate the association. Risk of bias was assessed through the Newcastle–Ottawa Scale. Subgroups and sensitivity analyses were performed using the RevMan 5.3 software. Publication bias was evaluated by Egger’s and Begg’s tests. Power analysis was conducted using the Power and Sample Size Calculation program. Results: Eight case–control studies containing 5,625 patients with OA and 5,600 healthy controls were obtained for the meta-analysis. After excluding cohorts with inadequate power, the pooled data supported that G allele carriers of rs12901499 had a significantly increased risk of OA (odds ratio 1.31, 95% CI: 1.21 to 1.43, P<0.00001). When stratified by OA site and ethnicity, the association remained statistically significant. Conclusion: The combined results evidently supported that rs12901499 polymorphism in SMAD3 gene is significantly associated with OA vulnerability across both Caucasian and Asian populations. Keywords: osteoarthritis, polymorphism, SMAD3, rs12901499, meta-analysis
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.037 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".