Coronal mode ultrasound guided hemodialysis cannulation: A pilot randomized comparison with standard cannulation technique
Bibliographic record
Abstract
BACKGROUND: Infiltrations from cannulation result in significant morbidity including loss of hemodialysis (HD) vascular access (VA). Cannulation is dependent on personnel skill and VA characteristics. Surface marking of VA lacks real-time information and traditional ultrasound (US) devices are large, expensive, requiring skilled operators. Sonic Window© (Analogic Ultrasound, Peabody, MA, USA) is a coronal mode ultrasound device (CMUD) approved for VA cannulation. METHODS: Single center randomized, prospective pilot study comparing handheld US-guided cannulation of new arteriovenous fistula (AVF) to standard cannulation practices. Patients with end stage renal disease (ESRD) on in-center HD who had a new AVF cleared for cannulation and dialysis were enrolled. Patients with new AVF received either standard cannulation (control group) or image guidance using CMUD (study group) for 3 weeks. Ultrasound characteristics of VA, cannulation practices and complications end points were obtained. RESULTS: An infiltration rate of 9.7% was noted during the study. Slightly lower odds ratio (OR) of infiltration was observed in the study group (OR 0.94, 95% CI: 0.26-3.41, P value = 0.93). Study group yielded longer time for assessment (101.8 ± 80.2 vs. 22.3 ± 22.5 seconds, P = < 0.001), increased cannulation time (41.1 ± 70.6 vs. 25.0 ± 27.9 seconds, P = 0.04), and increased patient satisfaction (94.6% vs. 82%, P = 0.04) compared to control group. Number of cannulation attempts, needle size, arterial or venous needle insertion, and tourniquet usage between groups were not statistically different. CONCLUSION: Handheld ultrasound is a safe and useful aid in cannulation of dialysis access.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".