MétaCan
Menu
Back to cohort
Record W4415820900 · doi:10.54531/mrlw9072

A76 A Hands-On Approach: Improving Trainee Confidence in Uterine Inversion and Postpartum Haemorrhage Management Through Low-Cost Simulation

2025· article· en· W4415820900 on OpenAlexaboutno aff
Elinor Robin Carlisle, Sarah Burgess

Bibliographic record

VenueJournal of Healthcare Simulation · 2025
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsUterine inversionTrainerSimulation trainingInversion (geology)Likert scaleIncidence (geometry)Session (web analytics)Uterus

Abstract

fetched live from OpenAlex

Introduction: Uterine inversion is a rare but life-threatening obstetric emergency, with its precise incidence remaining unclear [1]. A Canadian case series by Baskett suggests an incidence of 1 in 3,127 deliveries [2]. Associated with considerable morbidity and mortality, prompt recognition and management are essential to improving maternal outcomes [1]. Given its rarity, exposure to this emergency may not occur until later stages of clinical training. We aimed to develop a skills-based session using low-cost simulation models to support obstetric trainees in managing acute uterine inversion and associated postpartum haemorrhage (PPH). Methods: Four handmade uterine models were constructed from a mixture of felt, thread, velcro, cardboard, and wool. They represented key scenarios: inversion with adherent placenta, manual removal of placenta (MROP), atonic uterus for balloon tamponade, and a softly stuffed uterus for brace suture placement. Each model was integrated into a part-task pelvic trainer and used in a one-hour simulation session as part of an obstetric emergencies training day. Trainees (ST1–ST7) participated in small groups, facilitated by a registrar and consultant Obstetrician. The session included deliberate practice, structured discussions, and additional learning materials. Trainees completed all steps of uterine inversion management, MROP, and surgical control of PPH. Results: Feedback was obtained from 15 participants (n=15). 93.3% (n=14/15) rated the uterine inversion and MROP session as excellent and appropriate to their training level. Confidence levels, measured on a 5-point Likert scale (1= not at all confident to 5 = completely confident), increased from a pre-session mean of 3.1 to 4.2 following the session. Post-session, 87% (n=13/15) reported being fairly or completely confident, compared to 40% (n=6/15) beforehand. Similarly, 80% (n=12/15) rated the PPH surgical skills component as excellent and suitable for their training level. Confidence levels rose from a mean of 2.9 pre-session to 4.1 post-session, with 80% (n=12/15) feeling fairly or completely confident post-session, again up from 40% (n=6/15). Discussion: This low-cost, low-fidelity simulation, supported by expert facilitation, enabled participants to practice the management of a rare but critical emergency using a stepwise approach. Trainees across all grades reported improved confidence. While effective, the fabric models limited hydrostatic demonstration of the O’Sullivan technique. Future versions of the models will include enhanced anatomical features such as vasculature and adnexa to better simulate surgical procedures, including Huntington’s manoeuvre and emergency hysterectomy. Overall, this session achieved its educational objectives, was well received, and offers a reproducible model for future training. Ethics Statement: As the submitting author, I can confirm that all relevant ethical standards of research and dissemination have been met. Additionally, I can confirm that the necessary ethical approval has been obtained, where applicable. References 1. Bhalla R, Wuntakal R, Odejinmi F, Khan RU. Acute inversion of the uterus. The Obstetrician & Gynaecologist. 2009;11:13–18. Available from: https://obgyn.onlinelibrary.wiley.com/doi/10.1576/toag.11.1.13.27463#b6 2. Baskett TF. Acute uterine inversion: a review of 40 cases. J Obstet Gynaecol Can. 2002;24(12):953–956. Available from: https://pubmed.ncbi.nlm.nih.gov/12464994/

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.395
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Healthcare SimulationSame topicSimulation-Based Education in HealthcareFrench-language works237,207