Multiple single cannulation technique of arteriovenous fistula: A randomized controlled trial
Bibliographic record
Abstract
INTRODUCTION: Despite the impact needling has had on vascular access survival and patient outcome, there is no universal or standardized method proposed for proper cannulation. Rigorous studies are needed, examining cannulation practices, and challenges to achieving complication-free cannulation. METHODS: This randomized, open-label trial was conducted at 18 dialysis units owned by a large private dialysis provider operating in Portugal. Eligible patients were adults on chronic hemodialysis, with a new arteriovenous fistula (AVF); cannulated for at least 4 weeks complication-free. Patients were randomly assigned in a 1:1 ratio to one of three cannulation techniques (CT): Multiple Single cannulation Technique (MuST), rope-ladder (RLC), and buttonhole (BHC). The primary endpoint was AVF primary patency at 1 year. FINDINGS: One hundred seventy-two patients were enrolled between March 2014 and March 2017. Fifty-nine patients were allocated to MuST, 56 to RLC, and 57 to BHC. MuST and RLC were associated with a better AVF primary patency than BHC. Primary patency at 12 months was 76.3% in MuST, 59.6% in BHC, and 76.8% in RLC group. Mean AVF survival times were 10.5 months (95% CI = 9.6, 11.3) in the MuST group, 10.4 months (95% CI = 9.5, 11.2) in RLC, and 9.5 months (95% CI = 8.6, 10.4) in BHC. BHC was a significant risk predictor for AVF survival with 2.13 times more events than the other two CT (HR 2.13; 95% CI = 1.07, 4.21; p = 0.03). DISCUSSION: MuST was easy to implement without a diagram and there is no need to use blunt needles. This study showed MuST was efficacious and safe in maintaining the longevity of AVF in dialysis patients.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".