The effect of statin therapy on the formation of arteriovenous fistula stenoses and the rate of reoccurrence of previously treated stenoses
Bibliographic record
Abstract
Statins reduce inflammation in end-stage renal disease patients and improve endothelial function beyond cholesterol lowering. Despite this, statins do not improve the maturation rate, primary patency rate, and the cumulative survival of arteriovenous fistulas (AVFs). It is unknown if statins decrease the number of stenoses developing in AVFs or prolong the intervals between angioplasties needed to treat recurring stenoses. We conducted a retrospective chart review of our 265 active dialysis patients. The statin group was significantly more likely to be diabetic (64% vs. 43.6%) and treated with aspirin (64% vs. 40%) when compared to those not treated with statins (P=0.04 and 0.01). The mean time to first intervention (primary patency) was 16.5 months in statin users and 15.8 months in the nonstatin group (P=0.49) with standard deviations of ± 18.5 and 16.6 months, respectively. Statin use was not associated with a significant decrease in the number of stenoses diagnosed (P=0.28). The mean time between recurrent stenoses' angioplasties was 8.9 months in statin users and 7.3 months in the nonstatin patients (P=0.25). Aspirin users were more likely to have a decreased primary patency (rate ratio=1.65, P=0.03) compared with nonaspirin users. Patients who were prescribed aspirin developed 1.6 (P 0.01) times more stenoses than those not treated with aspirin. We report for the first time that statin therapy does not decrease the number of stenotic lesions developing in the AVF or prolong the interval between procedures required to treat recurrent stenoses.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".