Use of Crit‐Line Delta H Access Blood Flow in a Vascular Access Management Program to Decrease Episodes of Thrombosis and Increase URR
Bibliographic record
Abstract
Substantial morbidity occurs within the hemodialysis population due to complications of vascular access, most frequently manifests as thrombotic events. An access management goal was the reduction of thrombotic events and associated morbidity. Intra‐dialytic vascular access blood flow (ABF) measurements using Crit‐Line Delta H ABF have been previously shown to provide objective and accurate access flow data and were our primary method of evaluating access function. A designated ‘access manager’ was assigned responsibility to track all issues related to vascular access. Each patient's ABF was measured monthly and the flow ‘trend’ was graphed using Crit‐Line Access Manager software. Additional data, including auscultation, cannulation difficulties, failing URR, and increased venous pressures were also recorded. These data and ABF trends were used to establish an angiography ‘hot list’. Following angioplasty, ABF was again measured to confirm a successful intervention. The ‘hot list’ led to 43 angiography referrals of which 83.7% (36/43) required subsequent intervention resulting in significant reductions of thrombosis events in PTFE grafts and native fistulae. The annual incidence of thrombotic events decreased from 1.6 to 0.4 events/patient year in grafts and 0.5–0.0 events in fistulae. Additionally, the percentage of URR's > 70 increased from 82.3 to 90.1% over the same time period. Use of the Crit‐Line Delta H ABF device in conjunction with a vascular access management program can significantly reduce the number of thrombotic events per patient year.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".