Arterial Cannulation Simulation Training in Novice Ultrasound Users
Bibliographic record
Abstract
BACKGROUND: Arterial cannulation is an important procedure for hemodynamic monitoring and blood sampling. Traditional radial artery cannulation is performed by using anatomical knowledge and pulse palpation as a guide. Arterial cannulation using ultrasound (US) requires specific training, especially for new US users. We hypothesized that even for new US users, US guidance would facilitate the successful puncture by lower attempts before successful intraluminal cannulation of a simulation model of the radial artery. METHODS: A prospective randomized controlled crossover study was conducted with new US users on a gelatin phantom wrist. Three sessions of training were proposed: US-guided technique with low blood pressure (BP), palpation-guided technique with high BP, and one secondary comparison with low BP. For the 2 first sessions, all volunteers performed each technique but not in the same order. The main criterion was the number of attempts before successful catheterization of the model artery. A secondary criterion was the number of needle movements (the number of attempts plus the number of needle directional changes). RESULTS: < .001). All of the participants achieved success after the 12th needle movement for US technique, after the 19th needle movement for palpation high BP, and after the 25th needle movement for the secondary comparison, palpation low BP. The total time before success was not significantly different between the 2 first sequences (US vs palpation high BP). CONCLUSIONS: US technique was more successful than traditional palpation technique for novice US users performing arterial cannulations for the first time. A study in the clinical practice is needed to confirm these results.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".