183: High Fidelity Simulation Results in Improving Clinician Performance in the Management of Massive Hemorrhage Cases
Bibliographic record
Abstract
A massive haemorrhage can be a rare but serious complication of paediatric trauma and obstetrical cases. In order to optimize the management of massive haemorrhages, a protocol was implemented at our centre. However, recent audits showed that necessary improvements were to be made to many aspects of its application. To evaluate the application of a massive hemorrhage protocol and the ability to work in interdisciplinary teams using simulation and targeted training. We also aimed to evaluate confidence levels of different team members to apply the protocol and to hold their role in an interdisciplinary team during a crisis situation. Prospective observational study held at the simulation lab of a tertiary mother-child health care facility. Participants were nurses, respiratory technicians, orderlies, anesthetists, obstetricians, pediatric emergency physicians, pediatric intensivists and hematologists. Pediatric emergency/intensive care and obstetrical/anesthesia teams were submitted to high fidelity simulated pediatric trauma and post-partum massive hemorrhage scenarios respectively (Simbaby (Laerdal) and Noelle (Gaumard)). Each participant was asked to hold their usual role in an interdisciplinary team. Targeted training consisted of a debriefing session and a presentation reviewing the massive hemorrhage protocol as well as teamwork skills. All sessions were videotaped. Documents were given during the first session for future references and to prepare for the post session two weeks later. Confidence questionnaires were filled out during both sessions. Four blinded independent trained raters reviewed the videos and assessment was done using a checklist derived from the protocol and the Mayo High Performance Teamwork Scale. Means and standard deviations of scores for performances were calculated for each scenario and compared using an ANOVA test. Descriptive statistics for the confidence questionnaires were compared using a Mann Whitney test. A total of 62 healthcare professionals involved in eight interdisciplinary teams (four obstetrics/anaesthesia and four paediatric emergency/intensive care) as well as eight blood bank technologists and eight haematologists participated in the study. Following training, scores for the application of the protocol improved by 24% (95% CI 10 to 39). Scores for the ability to work in teams improved by 17% (95% CI 6 to 28). Confidence levels in the ability to apply the protocol and work in teams improved by 13% (95% CI 11 to 16). Targeted training involving HF simulated scenarios and protocol review improved participant ability to apply the massive haemorrhage protocol and to work in interdisciplinary teams. Confidence levels improved among participants from all disciplines.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".