OP15.04: Simulator‐based obstetric ultrasound training: a prospective randomised single‐blinded study
Bibliographic record
Abstract
Compare the use of simulator-based to patient-based obstetric ultrasound training. Prospective, randomised, single-blinded trial.18 trainees with minimal ultrasound exposure were recruited. Mid-trimester fetal brain anatomy in the standard planes: (BPD and HC, CSP, posterior fossa, and lateral ventricles) was chosen as a surrogate for all fetal anatomy ultrasound training. Trainees were randomised into 2 groups according to training method: Simulator group (“SIM”,n = 9) or Patient Group (“PT”,n = 9). The study design was as follows: a didactic session of the required planes, followed by ‘real’ patient 15 minute pre-test. 45 minute training session with dedicated ultrasound educator, using either Simulator or ‘real’ Patient. 15 minutes post-test: to obtain and label the standard 4 planes on a ‘real’ patient. All images were stored and then scored by 2 blinded MFM staff, according to 3 set criteria: image quality, landmarks and measurements. Each criterion was scored 0–15 for a total score of 0–60. Pre-test competence was similar between the groups. All trainees improved significantly (mean score pre-test 14.6 vs post-test 26.6, p < 0.05). For both groups a significant score improvement following training was found: PT (mean score pre-test 13.3 vs. post-test 24.6 p < 0.04) and SIM (mean score pre-test 15.9 vs. post-test 28.9 p < 0.05). Trainees were further divided by initial level of confidence (pretest score ≤5: very unconfident (VU); pretest >5: unconfident (U)). Improvement was similar for both groups but VU performance improved more in the SIM (mean pre vs. post test score 3.5 to 35) compared to PT (mean pre vs. post test score 2.3 to 25.6). Basic simulator based obstetric ultrasound training preformed as well as real patient training, and was found especially beneficial for beginner trainees. It may provide a solution in the form of a protected, accessible tool to help overcome the challenges of residency ultrasound training as well as improve patient care.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".