Optimizing Perimortem Cesarean Section Outcomes Using Simulation: A Technical Report
Bibliographic record
Abstract
Simulation-based medical education (SBME) is an educational technique that enables participants to experience an immersive representation of a clinical event for the purpose of practice, learning, and evaluation. This experience is intended to improve trainees' competency and confidence in both procedural tasks, as well as team-based and interpersonal skills when responding to real-world clinical encounters. Moreover, SBME improves procedural exposure and competency in low-frequency, high-stakes clinical procedures without the risk of adverse consequences, error, or patient harm - a priority for physician training at all levels. This technical report describes a novel bi-phasic maternal cardiac arrest simulation that can be used to teach and train post-graduate year one (PGY1) emergency medicine and obstetrics and gynecology trainees in the use of perimortem cesarean sections (PMCS) prior to in-situ exposure. Using a high-fidelity simulation protocol employing training manikins and 3-D printed models of gravid uteri, this bi-phasic simulation, completed over two sessions, six months apart, will equip trainees with the knowledge, skills, and professionalism behaviors necessary for difficult clinical decisions and time-critical procedures.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.001 |
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".