Improved neonatal outcomes by multidisciplinary simulation—a contemporary practice in the demonstration area of China
Bibliographic record
Abstract
Background Simulation-based training improves neonatal resuscitation and decreases perinatal mortality in low- and middle-income countries. Interdisciplinary in-situ simulation may promote quality care in neonatal resuscitation. However, there is limited information regarding the effect of multidisciplinary in-situ simulation training (MIST) on neonatal outcomes. We aimed to investigate the impact of MIST on neonatal resuscitation in reducing the incidence of neonatal asphyxia and related morbidities. Methods Weekly MIST on neonatal resuscitation has been conducted through neonatal and obstetrical collaboration at the University of Hong Kong-Shenzhen Hospital, China, since 2019. Each simulation was facilitated by two instructors and performed by three health care providers from obstetric and neonatal intensive care units, followed by a debriefing of the participants and several designated observers. The incidence of neonatal asphyxia, severe asphyxia, hypoxic-ischemic encephalopathy (HIE), and meconium aspiration syndrome (MAS) before (2017–2018) and after (2019–2020) the commencement of weekly MIST were analyzed. Results There were 81 simulation cases including the resuscitation of preterm neonates of different gestational ages, perinatal distress, meconium-stained amniotic fluid, and congenital heart disease with 1,503 participant counts (225 active participants). The respective incidence of neonatal asphyxia, severe asphyxia, HIE, and MAS decreased significantly after MIST (0.64%, 0.06%, 0.01%, and 0.09% vs. 0.84%, 0.14%, 0.10%, and 0.19%, respectively, all P < 0.05). Conclusions Weekly MIST on neonatal resuscitation decreased the incidence of neonatal asphyxia, severe asphyxia, HIE, and MAS. Implementation of regular resuscitation simulation training is feasible and may improve the quality of neonatal resuscitation with better neonatal outcomes in low- and middle-income countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".