Hemodialysis vascular access monitoring: Current concepts
Bibliographic record
Abstract
Most arteriovenous grafts fail due to irreversible thrombosis, and most clotted grafts have an underlying stenotic lesion. These observations raise the plausible hypothesis that early detection of graft stenosis with preemptive angioplasty will reduce the likelihood of graft thrombosis. A number of noninvasive methods can be used to detect hemodynamically significant graft stenosis with a high positive predictive value. These tests include clinical monitoring, as well as surveillance by static dialysis venous pressures, flow monitoring, or duplex ultrasound. However, these surveillance tests have a much lower positive predictive value for graft thrombosis in the absence of preemptive angioplasty. In other words, none of the currently available surveillance tests can reliably distinguish between stenosed grafts destined to clot, and those that will remain patent without intervention. As a consequence, any program of graft surveillance necessarily results in a substantial proportion of unnecessary angioplasties. Moreover, a substantial proportion of grafts thrombose despite a normal antecedent surveillance test. Numerous observational studies have found an impressive reduction of graft thrombosis after implementation of a stenosis surveillance program. In contrast, 5 of 6 randomized clinical trials failed to show a reduction of graft thrombosis in patients undergoing graft surveillance, as compared with those receiving only clinical monitoring. The lack of benefit of surveillance is largely attributable to the rapid recurrence of stenosis after angioplasty. Thus, routine surveillance for graft stenosis, with preemptive angioplasty, cannot be recommended for reduction of graft thrombosis. Future research should be directed at pharmacologic interventions to prevent graft stenosis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".