Hands-on Simulation Workshop for Obstetric Ultrasound-Guided Invasive Procedures
Bibliographic record
Abstract
Introduction: Due to the introduction of cell-free DNA genetic testing options, the number of clinical ultrasound procedures has greatly diminished in recent years. With fewer real-life ultrasound-guided procedures being performed, it is difficult for OB/GYN and maternal-fetal medicine (MFM) trainees to achieve competency in doing them. Simulation can be utilized to address this issue and supplement a learner's real-life training. Methods: We developed a simulation workshop incorporating previously described ultrasound guidance task trainers and simulators of amniocentesis and chorionic villus sampling. The workshop had three parts: needle guidance basics, targeting task performance, and procedure-specific simulation. A form of this workshop has been held at the annual meeting of the Society for Maternal-Fetal Medicine since 2015 and as a regional course for MFM fellows since 2017. During the 2019 and 2020 courses, participants completed Likert-scale surveys evaluating the course. Results: Since the workshops began in 2015, approximately 300 people have participated. In 2019-2020, 41 MFM attending physicians, 136 MFM fellows, and three OB/GYN residents took our course and completed a postcourse survey. Participants rated the course highly and thought it was highly effective. Discussion: We created an introductory simulation workshop for obstetric ultrasound-guided invasive procedures that participants rated highly and thought was very effective. Objective clinical assessment of skill improvement after completion of this course is needed to verify its true impact. Repeated exposure to this introductory simulation and creation of more challenging workshops are needed to achieve a sustained high level of procedural skill.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".