Umbilical Cord Prolapse in a Labouring Patient: A Multidisciplinary and Interprofessional Simulation Scenario
Bibliographic record
Abstract
This case is one of an eight-case multidisciplinary curriculum designed and implemented at the University of Ottawa by simulation educators with specialty training in obstetrics and gynecology (OB/GYN) and anesthesiology. Consultation from a nurse educator maintained quality and relevance of objectives for nursing participants. The curriculum was prepared to train OB/GYN and anesthesiology residents and nurses to hone crisis resource management skills and to recognize and manage rare/critical medical events in an obstetrical setting. Obstetricians, anesthesiologists, and nurses often work together in acute, high-stakes situations, and this curriculum provides a safe environment to practice team-based management of such emergencies. Over an eight-year period, this curriculum has been executed in scenario couplets on a four-year cycle to allow OB/GYN and anesthesiology residents exposure to all scenarios during a five-year residency beginning in their second year. Prospective evaluative data has been positive. For example, over 90% of participants rated these simulations to be 5 out of 5 with comments, such as "Was an effective use of my educational time" and "Will influence/enhance my future practice". In this scenario, participants must recognize and manage fetal distress resulting from umbilical cord prolapse in a labouring patient and respond with urgent operative delivery. This scenario requires adult and fetal mannequins with presenting umbilical cord for pelvic examination as well as equipment for fetal monitoring, general anesthetic, and emergency cesarean section. This simulation case includes a case template, critical actions checklist, debriefing guide, summary of key medical content, and an evaluation form for learners to provide feedback.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".