The native arteriovenous fistula in 2007 Surveillance and monitoring
Bibliographic record
Abstract
In the past 5 years, some clinical trials have questioned the value of surveillance in managing vascular accesses. Although prolongation of access life span is an important end point, reduction of thrombotic events reduces patient risks resulting from loss of access patency. Most of the available evidence suggests that detection of stenosis and prevention of thrombosis is valuable. When a test indicates the likely presence of a stenosis, then venography or fistulography should be used to definitively establish the presence and degree of the stenosis. In most but not all cases, angioplasty should be performed if the stenosis is greater than 50% by diameter. The value of routine use of any surveillance technique for detecting anatomic stenosis alone, without concomitant functional assessment by measurement of access flow, venous pressure, recirculation or other physiologic parameters, has not been established. Stenotic lesions should not be repaired merely because they are present. If such correction is performed, then intraprocedural or periprocedural measurement of access flow (QA) or intra-access pressure should be conducted to demonstrate a functional improvement with a successful percutaneous transluminal angioplasty.
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 | 0.005 |
| 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 | 0.000 | 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".