Understanding Surgical Preference and Practice in Hemodialysis Vascular Access Creation
Bibliographic record
Abstract
Understanding healthcare providers' preferences, values, and beliefs around AVF eligibility is important to explain variability in practice. We conducted a survey of international surgeons, using hypothetical patient scenarios, to assess resources used, variables, perceived barriers, and absolute contraindications to access creation. A total of 134 surgeons completed the survey. Venous duplex ultrasound mapping (VDUM) was offered to all patients by 90% of US, 68% Canadian, and 63% European respondents. VDUM altered clinical decision less than 25% of the time for 33% American, 48% Canadian, and 85% European surgeons. Increased comorbidities and previous failed access were deterrents to AVF creation as was vessel size. Second choice access was the AV graft in the US and Europe and the catheter in Canada. Absolute contraindications to AVF creation included patient life expectancy <1 year, left ventricular ejection fraction (LVEF) <15%, and a history of dementia, while 42% surgeons reported no absolute contraindications. Perceived barriers included patient preferences, long wait times for surgery, and late referral to a Nephrologist. Significant variability exists in the surgical preoperative assessment of patients, and the eligibility criteria used for fistula creation. Understanding surgeons' preferences can aid in establishing standardization for VA access eligibility, including surgical assessment.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".