In‐plane guided upper arm arteriovenous fistula cannulation with color ultrasound
Bibliographic record
Abstract
BACKGROUND: The blood vessel in the upper extremity arteriovenous fistula (AVF) is deep in the tissue, and cannulation in AVF is frequently associated with blood oozing, hematoma, or aneurysm. This study evaluated the performance of color ultrasound in-plane guided cannulation technique during upper extremity high-AVF cannulation in patients with hemodialysis. METHODS: A total of 40 patients with hemodialysis who needed cannulation in upper extremity AVF were recruited in the study, and the patients were randomly divided into observation group and control group. Color Doppler ultrasound was used to guide cannulation in the observation group and in the control group blind cannulation method was applied. The success rate of one-time cannulation, the incidence of subcutaneous hematoma, oozing, and pain caused by incorrect fistula cannulation as well as the satisfaction score of the patients were compared to evaluate the effect and advantages of color ultrasound-guided cannulation. RESULTS: The one-time success rate of internal fistula cannulation in the observation group (98.71%) was significantly higher than that in the control group (88.27%). The incidence rates of hematoma, oozing, pain, and total failure events were significantly reduced in the observation group. The average satisfaction degree in the observation group was also significantly higher than that of the control group. CONCLUSION: Ultrasonic-guided cannulation effectively enhances the success rate of cannulation in upper extremity AVF, reduces the incidence of cannulation failures, and improves the satisfaction level in the patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".