The Role of Simulation in Knowledge Dissemination and Improving Clinical Outcomes for the Management of Impacted Fetal Head at Cesarean Delivery
Bibliographic record
Abstract
OBJECTIVES: Impacted fetal head (IFH) is an obstetrical emergency associated with maternal and fetal morbidity. The aim of this study was to assess the role of a simulation session on the management of IFH at cesarean delivery as a method of disseminating guideline recommendations to obstetricians. The secondary aim was to assess the clinical impact of this initiative. METHODS: An IFH simulation session based on recent guidelines was developed using the PROMPT FLEX enhanced cesarean delivery model to review pull, push, and Patwardhan techniques. All faculty and obstetrical trainees were invited to participate. Participants completed baseline and post-session surveys. Survey scores were compared using a paired t-test and analysis of variance. To assess clinical impact, a 6-month pre/post cohort of all patients who underwent second-stage cesarean delivery was assembled. Multivariable robust Poisson regression models were used to estimate the relative risk of a composite maternal/neonatal morbidity outcome pre-and post-implementation. RESULTS: Thirty-four clinicians participated (21 trainees, 13 faculty). Trainees demonstrated significant, sustained improvement in knowledge following participation (P < 0.001). Trainees reported sustained comfort with push and pull techniques, but comfort with Patwardhan waned (P = 0.03). Faculty comfort was unchanged for push and pull techniques but significantly improved for Patwardhan (P = 0.01). The adjusted relative risk of composite maternal/neonatal morbidity following implementation was unchanged (relative risk 1.03; 95% CI 0.75-1.42). However, neonatal intensive care unit admissions were reduced (P = 0.01). CONCLUSIONS: An IFH simulation session was associated with improvement in trainee knowledge and faculty and trainee comfort with the Patwardhan technique. Simulation may be an effective tool to improve obstetrician comfort with novel recommendations.
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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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".