Impact of Arteriovenous fistula creation on estimated glomerular filtration rate decline in Predialysis patients
Bibliographic record
Abstract
Abstract Background Arteriovenous fistula (AVF) is the vascular access of choice for patients on hemodialysis. Recent evidence suggests that AVF creation may slow estimated glomerular filtration rate (eGFR) decline. The study objective was to assess the impact of the AVF creation on eGFR decline, after controlling for key confounding factors. Methods This retrospective cohort study included adult patients followed in a single-center predialysis clinic between 1999 and 2016. Patients with a patent AVF were followed up to 2 years pre- and post-AVF creation. Estimated GFR trajectory was reported using linear mixed models adjusted for demographic characteristics, comorbidities and use of renin-angiotensin-aldosterone blockade. Results A total of 146 patients were studied with a median age 68.7 (60.5–75.4) years and a median eGFR at time of AVF creation of 12.8 (11.3–13.9) mL/min/1.73m 2 . The crude annual eGFR decline rates were − 3.60 ± 4.00 mL/min/1.73 m 2 pre- and − 2.28 ± 3.56 mL/min/1.73 m 2 post-AVF, resulting in a mean difference of 1.28 mL/min/1.73 m 2 (95% CI 0.49, 2.07). In a mixed effect linear regression model, monthly eGFR decline was − 0.63 (95% CI -0.81, − 0.46; p < 0.001) mL/min/1.73m 2 /month. The period after AVF creation was associated with a relatively higher eGFR (β 0.94, 95% CI 0.61–1.26, p < 0.001). There was a significant association between follow-up time and the period pre/post AVF (β 0.19, 95% CI 0.16, 0.22; p < 0.001) such that eGFR decline was more attenuated each month after AVF creation. Conclusions In this cohort, AVF creation was associated with a significant reduction of eGFR decline. Further prospective studies are needed to confirm this association.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".