Technical Challenges When Performing Ultrasound‐Guided Peripheral Intravenous Placement in Children
Bibliographic record
Abstract
Ultrasound (US) guidance has become an essential skill for peripheral intravenous (PIV) placement in children. It may be used as a primary approach or as a rescue technique after failed attempts, particularly in children with difficult intravenous access (DiVA). An increasing amount of literature shows the benefit of using US guidance for PIV placement in children with DiVA. With more consistent availability of US machines across many institutions, their use for PIV placement is becoming commonplace, with several types of clinicians performing the procedure. Novice proceduralists and those in training may encounter technical challenges that may impede successful PIV cannulation. Having strategies to avoid, troubleshoot, and overcome technical challenges is essential for improving the technique of US guidance for PIV access. The purpose of this review was to summarize the literature around the most common technical challenges that arise when performing US-guided PIV placement in children and practical strategies that may improve cannulation success. We also highlight US-guided PIV placement considerations specific to special populations, including premature neonates, pediatric burns, epidermolysis bullosa, and those receiving bleomycin sclerotherapy.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".