A pilot study of genetic polymorphisms and hemodialysis vascular access thrombosis
Bibliographic record
Abstract
Vascular access thrombosis (VAT) remains a significant problem worldwide. This study determined the association between VAT and 7 candidate gene polymorphisms (factor V Leiden 1691G>A, factor II 20210G>A, methylenetetrahydrofolate reductase 677C>T, angiotensin converting enzyme 287 base pair (bp) insertion/deletion, transforming growth factor-beta1 869T>C and 915G>C, NOS3 -786T>C and intron 4 27 bp tandem repeat, and endotoxin receptor CD14 -159C>T). This was a retrospective case-control pilot study conducted in 101 hemodialysis patients at a large tertiary-care, University health-science center. Sixty cases that experienced frequent VAT and 41 controls that had not experienced VAT in at least 3 years were evaluated for demographics and genotyping. These data were summarized, and univariable and multivariable regression models were constructed. Univariate VAT predictors included the NOS3 420 bp allele (P=0.03) and the presence of a central venous dialysis catheter (P<0.01). Aspirin use was protective against VAT (P=0.02). In the multivariate analysis, the dialysis access type remained a significant predictor of thrombosis (P<0.01), while aspirin use retained its protective status (P=0.01). Statin use was associated with the cases (P=0.02); however, the NOS3 420 bp allele failed to improve the model. These data confirm that central venous dialysis catheter access is associated with thrombosis, while aspirin use appears protective. The NOS3 420 bp allele may have an association with thrombosis; however, further epidemiologic data evaluating large dialysis registries are needed to confirm our observation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".