Low-Cost Task Trainer for In Utero Fetal Stent Placement
Bibliographic record
Abstract
INTRODUCTION: Some fetal procedures such as intrauterine fetal stent placement remain rare, and simulation is needed to help learners and specialists in attaining and maintaining technical competence. We sought to design and assess a low-cost, easily assembled yet clinically relevant task trainer for fetal stent placement. METHOD: The simulator was constructed using 2 quart-sized freezer bags filled with ultrasound gel and sealed with clear packing tape. The bags were stacked vertically in a transparent plastic container with ultrasound gel applied between the bags when ultrasound was used. This task trainer was used to deploy in utero stents with or without the use of ultrasound. It has been used at the annual meeting of the Society for Maternal-Fetal Medicine since 2015, the annual meeting of the International Society of Ultrasound in Obstetrics and Gynecology in 2015 and 2016, and at regional Maternal-Fetal Medicine Fellow simulation workshops since 2016. Participants were asked to complete a 5-point Likert scale survey regarding the model's realism and usefulness in training. RESULTS: One hundred thirty-three course participants evaluated the task trainer. The median rating for realism of the ultrasound images, haptic feel of stent deployment, and usefulness in training was 5 (interquartile range, 4-5). Seven physicians participated in the timed assessment of model assembly, stent deployment, and model reassembly. The average times required for the freezer bag task trainer were 2.3 minutes (2.20-2.35), 1.0 minutes (0.70-1.93), and 0.1 minutes (0.08-0.10), respectively. For the porcine tissue-based model tested in parallel, the average times were 6.0 minutes (5.00-7.06), 3.7 minutes (3.63-3.75), and 3.3 minutes (3.00-3.70), respectively. CONCLUSIONS: This low-cost simulator was rated highly when used to practice in utero stent deployment and allows for numerous repetitions in each training session. It could be a valuable tool in training novice providers and allow more experienced providers to maintain competence in this low-volume procedure.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".