Ultrasound‐guided techniques for peripheral intravenous placement in children with difficult venous access
Bibliographic record
Abstract
Peripheral intravenous placement in children can be challenging. Different techniques have been used to improve first pass success rates in children with known history of difficult venous access including surface landmarking, local warming, transillumination, ultrasonography, epidermal nitroglycerin, central venous access, intraosseous placement, and venous cutdown. Among these, ultrasound guidance has garnered the most interest among anesthesiologists. The cumulative literature surrounding the utility of ultrasound-guided peripheral intravenous placement in children with and without difficult venous access has shown mixed results. Literature on the utility of ultrasound guidance for peripheral intravenous placement in children under deep sedation or anesthesia is limited but encouraging. This review summarizes the overall evidence for ultrasound-guided peripheral intravenous placement in children with difficult venous access under deep sedation or general anesthesia. Furthermore, five subtly varying approaches to ultrasound-guided peripheral intravenous placement with their advantages and disadvantages will be discussed. One of these five approaches is Dynamic Needle Tip Positioning. Utilizing a short axis out of plane ultrasound view, this promising technique allows for accurate needle tip localization and may increase the success rate of peripheral intravenous placement, even in small children, under deep sedation, or general anesthesia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".