Mono- and bi-plane sonographic approach for difficult accesses in the emergency department – A randomized trial
Bibliographic record
Abstract
BACKGROUND: The insertion of peripheral intravenous (PIV) catheters is one of the most performed invasive procedures in acute healthcare settings. However, peripheral difficult vascular access (PDVA) is not uncommon and can lead to delays in administering essential medications. Ultrasound (US) has emerged as a valuable tool for facilitating PIV cannulation. Advancements in technology have introduced a technique known as bi-plane imaging, allowing the simultaneous display of both longitudinal and transverse views of vessels. We aimed to investigate whether the utilization of bi-plane imaging, as opposed to the single-plane approach, would yield superior results for PDVA in the emergency department (ED). METHODS: This study was a single-center randomized controlled trial. We included adult patients admitted to the ED who required PIV cannulation. Patients were randomly assigned to undergo cannulation using either the mono-plane or bi-plane approach, both performed by skilled providers. The primary outcome of the study was to compare the first attempt success rates between the two techniques. RESULTS: A total of 442 patients were enrolled, with 221 undergoing cannulation attempts using the mono-plane approach. Successful placement of a functioning PIV catheter was achieved in a single attempt for 313 out of 442 patients (70.8%). There was no significant difference in the success rates between the two study groups: 68.3% in the mono-plane group and 73.3% in the bi-plane group (p = 0.395). The median time required for a successful attempt differed between the groups, with 45 s (range 18-600) in the mono-plane group and 35 s (range 20-600) in the bi-plane group (p = 0.03). CONCLUSIONS: Our study confirms that US is a highly effective tool for facilitating PIV cannulation in patients with PDVA presenting to the ED. However, our investigation into the use of bi-plane imaging did not reveal a significant improvement when compared to mono-plane imaging.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".