Reducing vascular access morbidity: a comparative trial of two vascular access monitoring strategies
Bibliographic record
Abstract
BACKGROUND: Thrombosis is the primary cause of access failure in polytetrafluoroethylene grafts and arteriovenous fistulas. It can lead to significant patient and access morbidity and mortality, and is difficult to prevent medically. Intervention is largely limited to maximizing access patency by detecting culprit lesions early and intervening with angioplasty or surgical revision. The most efficacious monitoring strategy is undetermined. METHODS: This 3 year prospective study took advantage of a change in monitoring strategy used in a large dialysis centre to compare the efficacy of two methods used to monitor grafts and fistulas in order to prevent access thrombosis. Accesses were monitored using Duplex ultrasonography in year 1, while the saline ultrasound dilution technique (Transonic) became the primary monitoring strategy in year 3 (year 2 was a transition year). Risk factors for thrombosis were determined using multivariate survival analysis, and the performance of Duplex ultrasonography and Transonic monitoring was assessed. RESULTS: A total of 303 656 access days at risk were assessed, with 344, 385 and 425 accesses in years 1, 2 and 3, respectively. The total thrombosis rate was 1.01/1000 access days in year 1 compared with 0.66/1000 access days in year 3. This was accomplished despite a reduction in procedure rates of 55% for angiograms, 13% for angioplasties and 31% for thrombolysis. CONCLUSION: Low flow rates detected using Transonic monitoring were associated with increased thrombosis, while stenosis detected using Duplex ultrasonography was not a strong predictor of incipient thrombosis; however, these different access characteristics were compared using monitoring techniques that may be ideal in different clinical situations.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".