Standardising Simulation‐Based Obstetric Emergency Training: A Delphi Consensus Study
Bibliographic record
Abstract
OBJECTIVE: To develop standardised Objective Structured Assessment of Technical Skills (OSATS) forms for major obstetric emergencies, thereby improving the quality and consistency of obstetric simulation training. DESIGN AND SETTING: A panel of national experts with extensive experience in teaching Gynaecology and Obstetrics, simulation training, and the clinical management of labour complications and peripartum emergencies. POPULATION AND METHODS: A Delphi process with four iterative rounds was conducted to create, evaluate, revise, and finalise OSATS checklists for 11 obstetric emergencies. Each OSATS form was rated using a Likert scale (0-9), refined according to expert feedback, and validated through structured discussions. MAIN OUTCOME MEASURES: The creation and approval of OSATS forms for shoulder dystocia, vacuum delivery, assisted breech delivery, third- and fourth-degree laceration repair, external cephalic version, abnormal CTG management, postpartum haemorrhage, non-cephalic second twin delivery, reverse breech extraction at caesarean section, maternal collapse and forceps application. RESULTS: Consensus was achieved for all emergencies with good to excellent ratings: shoulder dystocia (82%), external cephalic version (94%), vacuum delivery (75%), abnormal CTG management (42%), postpartum haemorrhage (96%), reverse breech extraction (72%), maternal collapse (94%), forceps application (76%), non-cephalic second twin delivery (96%), assisted breech delivery (94%) and third- and fourth-degree laceration repair (82%). CONCLUSION: The Delphi study successfully developed consensus-based OSATS forms, addressing the need for standardised assessments in obstetric simulation training. These tools enhance training quality, identify skill gaps and improve clinical preparedness. This study was supported by AGUI (Associazione Ginecologi Universitari Italiani).
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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 | 0.277 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".