Teaching invasive perinatal procedures: assessment of a high fidelity simulator‐based curriculum
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Learning curves pose a difficult problem in the teaching of technical skills: how do you teach procedural skills without compromising patients' health? A simulator-based curriculum has been designed to minimize the risks to patients undergoing amniocentesis by shifting the learning curve away from patients and into the laboratory. This study evaluated the effectiveness of a high-fidelity simulator-based curriculum in improving the performance of amniocentesis by obstetric trainees. DESIGN: Thirty trainees received a course on the practice of amniocentesis. The curriculum consisted of a lecture, a syllabus, and a hands-on training session with the simulator. Pre- and post-training performance were evaluated with two rating scales. Training and performance evaluation were completed using the same simulator. The effectiveness of the simulator-based workshop and the effect of year of training were assessed using a two-way analysis of variance. RESULTS: Performance scores improved from a mean score of 55% to 94% using checklist scoring and from 57% to 88% using global ratings. The two-way analysis of variance revealed a significant effect of training (F1,60 = 43.57; P < 0.001) accounting for 45% of the variance in scores, and a significant effect of experience level (F2,60 = 9.16; P < 0.001) accounting for 25% of the variance in scores. CONCLUSIONS: A comprehensive curriculum based on a high-fidelity simulator was effective at improving skills demonstrated on the simulator. The challenge remains to establish that skills acquired on a simulator are transferable to the clinical setting.
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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 | 0.044 |
| 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 it