EP27.38: Is the use of the ScanTrainer ultrasound simulator relevant to the continuous professional development of obstetricians and gynecologists?
Bibliographic record
Abstract
To assess the interest and satisfaction of obstetricians and gynecologists (OB/GYNs) with the use of an ultrasound simulator as part of a multimodal continuous professional development (CPD) training; Écho-Réalité. Twenty-four OB/GYNs ultrasonographers with different experience levels, participating in the training; “Écho-Réalité: Second trimester ultrasound” were enrolled in our study. This two days multimodal ultrasound CPD training includes theoretical, reflexive and practical sessions on real patients in a simulated and workplace context. Participants had access to a ScanTrainer simulator (Medaphor) on a voluntary basis throughout the formation. Their ability level and time spent on the simulator were assessed. Participants were asked to fill out an online 32-questions satisfaction survey at the end of each day. Eighteen (77%) participants used the simulator during their training and all participants filled out the satisfaction surveys. Participants spent an average of 6 minutes (min 2; max 10) using the ultrasound simulator. The average time to correctly complete a clinical case was 1.34 minutes (min 0.5; max 2). The overall Écho-Réalité training obtained very high satisfaction levels of 96% and 100% for training in a simulated and workplace context, respectively. Of those who used the simulator, 62% agreed that this device was a useful learning tool at their level of expertise, while 38% disagreed with this statement. Only 21% of participants found the simulator experience to be a good equivalent to real patient practice, or satisfactory for their CPD expert training. The integration of ultrasound simulators in CPD appears of limited interest for OB/GYNs ultrasonographers when practice on live/real patients is available. Further trials should investigate the interest in, and efficiency of ultrasound simulators for CPD training as a preparatory step, or separate activity without the access to a real patient model. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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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 | 0.011 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".