Implementation and Evaluation of Training for Ultrasound-Guided Vascular Access to Small Vessels Using a Low-Cost Cadaver Model
Bibliographic record
Abstract
OBJECTIVES: Critically ill neonatal and pediatric patients often require central vascular access. Real-time ultrasound guidance for central venous catheterization is beneficial. Because the diameter of central veins is much smaller in neonates than in adults, extensive training is needed to master the visualization and catheterization of central veins in neonates. This study assessed the learning effect of a standardized simulation-based teaching program on ultrasound-guided cannulation in a low-cost cadaver tissue model. DESIGN: This simulation-based prospective study assessed physician competence in the ultrasound-guided central venous catheterization procedure. Analyses were conducted before and after the teaching course. SETTING: Pediatric simulation center at a tertiary care center. SUBJECTS: Staff physicians from the Neonatal ICU and PICUs at the Medical University of Vienna. INTERVENTIONS: Two latex tubes, with internal diameters of 2 and 4 mm, were inserted in parallel into cadaver tissue to mimic vessels and create a model for central venous catheterization. MEASUREMENTS AND MAIN RESULTS: Under ultrasound guidance, each participant attempted to puncture and insert a guide-wire into each of the latex tubes using in-plane and out-of-plane techniques, both before and after the teaching course. The training program was assessed using a questionnaire and a performance checklist. Thirty-nine physicians participated in this study. The rates of failure of guide-wire insertion into 2-mm tubes were significantly lower after than before the teaching course, using both in-plane (p = 0.001) and out-of-plane (p = 0.004) techniques. Teaching, however, did not significantly reduce the insertion failure rate into 4-mm tubes, either in-plane (p = 0.148) or out-of-plane (p = 0.069). The numbers of successful cannulations on the first attempt increased after the teaching in all methods (p = 0.001). CONCLUSIONS: Implementation of a skills training program for ultrasound-guided central venous catheterization in a cadaver tissue model was feasible and cost- and time-effective. The number of attempts until successful cannulation of small vessels (2-mm tube) was significantly lower after than before the standardized teaching program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".