The risk of dialysis access thrombosis is related to the transforming growth factor–β1 production haplotype and is modified by polymorphisms in the plasminogen activator inhibitor–type 1 gene
Bibliographic record
Abstract
Transforming growth factor-beta1 (TGF-beta1) and plasminogen activator inhibitor-type 1 (PAI-1) might play a role in the development of fibrosis and stenosis of hemodialysis vascular accesses. We studied polymorphisms in the TGFbeta1 (869T>C; 915G>C), and PAI-1 (4G/5G) genes in 416 hemodialysis patients (107 access thrombosis cases, 309 controls), to determine if they are related to vascular access thrombosis. Three TGF-beta1 production haplotypes (low, intermediate, and high) were defined according to the combination of polymorphisms found. The adjusted odds ratio (OR) and 95% confidence interval (CI) for access thrombosis in low TGF-beta1 producers was 7.31 (2.15-24.88; P = .001). The interaction between low TGF-beta1 production haplotype and the 4G/4G PAI-1 genotype was strongly associated with access thrombosis (adjusted OR 19.3; 95% CI 2.82-132.40; P = .003). Mean access thrombosis-free survival times in years (95% CI) were 14.65 (12.05-17.25), 11.96 (8.67-15.25), and 4.94 (3.06-6.83) in high, intermediate, and low TGF-beta1 producers, respectively (P = .044). Analysis of the synergy index and the case-only cross-product supported the presence of an interaction. We concluded that low TGF-beta1 production haplotype is a risk factor for hemodialysis access thrombosis and that in the presence of the 4G/4G PAI-1 genotype there is an additional increase in risk.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".