Trauma resuscitation: can team behaviours in the prearrival period predict resuscitation performance?
Bibliographic record
Abstract
Background: Optimising team performance is critical in paediatric trauma resuscitation. Previous studies in aviation and surgery link performance to behaviours in the prearrival period. Objective: To determine if patterns of human behaviour in the prearrival period of a simulated trauma resuscitation is predictive of resuscitation performance. Design: Twelve volunteer trauma teams performed in four simulation scenarios in a paediatric hospital. The scenarios were video recorded, transcribed and analysed in 10-second intervals. Variation in the amount of utterances per team member in the prearrival period was compared with team performance and implicit coordination during the resuscitation. Key results: Coders analysed 18 962 s of video. They coded 5204 team member utterances into one of eight communication behaviour categories. Inter-rater reliability was excellent (an average of 83.1% across all four scenarios). The average number of communications occurring during the prearrival period was 18.84 utterances, with a range of 2-42 and a SD of 9.55. The average length of this period was almost 2 minutes (mean =117.30 s, SD=39.20). Lower variance in team member communication during the prearrival better was associated with better implicit coordination (p=0.011) but not team performance (p=0.054) during the resuscitation. Conclusion: Patterns of communication in the prearrival trauma resuscitation period predicted implicit coordination and a trend towards significance for team performance which suggests further studies in such patterns are warranted.
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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 | 0.002 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".