Screening for Subclinical Stenosis in Native Vessel Arteriovenous Fistulae
Bibliographic record
Abstract
Guidelines recommend the use of ultrasound dilution techniques (UDT), including measurement of access recirculation (AR) and access blood flow (Q(a)), to screen for subclinical vascular access dysfunction. Although these techniques are efficacious in polytetrafluoroethylene grafts, data in native vessel arteriovenous fistulae (AVF) are lacking. A prospective observational study was conducted to evaluate the utility of UDT screening in AVF. Q(a) and AR were measured bimonthly. Positive studies required fistulograms and were defined by Q(a) < 500 ml/min, DeltaQ(a) > 20% from baseline or AR > 5%. Accesses with stenosis underwent percutaneous angioplasty. After 1 yr, there were 1355 mo of follow-up in 177 patients. There were 44 positive studies in 40 patients. Q(a) was <500 ml/min in 36 (82%), DeltaQ(a) was >20% in 5 (11%), and AR was >5% in 6 (14%). Of patients with Q(a) < 500 ml/min, 29 (81%) had stenosis. Only two patients (40%) with DeltaQ(a) > 20% but Q(a) > 500 ml/min had stenosis. No patient with AR > 5% had stenosis unless Q(a) was also <500 ml/min. Immediate patency rate was 93% post-PTA. Mean Q(a) increased from 303 +/- 154 ml/min to 602 +/- 220 ml/min (P < 0.0001), and mean urea reduction ratio increased from 70.4 +/- 8.4% to 74.6 +/- 6.5% (P = 0.003) post-PTA. The results demonstrate that UDT could detect subclinical stenoses in AVF, and most lesions were amenable to angioplasty. AVF that underwent PTA delivered higher Q(a) and urea reduction ratio, and immediate patency rates were acceptable. Access failure after negative UDT was unusual. Measuring AR increases the time required to perform UDT but does not improve utility. Serial measurements of Q(a) alone may be the best strategy for screening AVF.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".