Training novice in ultrasound-guided venipuncture: A randomized controlled trial comparing out-of-plane needle-guided versus free-hand ultrasound techniques on a simulator
Bibliographic record
Abstract
Background: Peripheral intravenous access is a common medical procedure, however, it can be difficult to perform in some patients. Success rates have proved greater with ultrasound guidance. Peripheral intravenous access using ultrasound requires specific training, especially for new ultrasound users. To overcome these difficulties, guidance devices on ultrasound probes are able to control the angle of penetration into tissues. We hypothesized that, and particularly for new ultrasound users, the use of a needle guide (NG) paired with the out-of-plane approach would facilitate puncture of a simulation model of vessel more effectively than similar free hand (FH) techniques. Methods: A prospective controlled randomized study was conducted of new ultrasound users using a guide wire introducer needle on gelatine phantom. After a 30-min lecture, one group performed the FH technique and the other group performed the NG technique both in an out-of-plane approach. The main criterion was the number of attempts before success of catheterization of this model of vessel. Results: Thirty-four nurse anesthetist students participated in the study. The number of attempts before success using the NG technique was significantly lower: 3.7 (±0.9) in the NG group versus 6.7 (±3.3) in the FH group ( p = 0.01). In the NG group, 100% of the participants achieved success after the sixth attempt. In the FH group, only 81.25% ( n = 13/16) reached success. Conclusion: NG technique has been proved to have a steeper learning curve compared with the FH technique. A study on a learning curve in clinical practice is needed to confirm these results.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".