Pre-alerts from critical care ambulances to trauma centers: a quantitative survey of trauma team leaders in Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: Pre-alerts from paramedics to trauma centers are important for ensuring the highest quality of trauma care. Despite this, there is a paucity of data to support best practices in trauma pre-alert notifications. Within the trauma system of Ontario, Canada, the provincial critical care transport organization, Ornge, provides pre-alerts to major trauma centers, but standardization is currently lacking. This study examined the satisfaction of trauma team leaders' (TTLs) satisfaction with current trauma pre-alerts and their preferences for logistics, content, and structure. METHODS: This was a quantitative survey of TTLs at adult and pediatric trauma centers across Ontario, Canada. Recruitment was through email to trauma directors, with follow-up efforts to target low-response sites to achieve good geographical representation. The survey was completed online and contained a combination of single or multiple-choice questions, Likert scales and free text options. RESULTS: In total, 79 TTLs from adult and pediatric lead trauma centers across Ontario responded to the survey, which took place over a 120-day period. The survey achieved good geographical representation. Given the current processes, TTLs describe moderate satisfaction with room for improvement (median score 3, IQR 3-4 on a 5-point Likert scale). Their overall preference was for timely and direct communication, with some concerns about multiple channels of communication around logistics. Most TTLs agreed on the important and less important content details found in common standardized framework tools. For structure, 28/79 TTLs strongly preferred the cognitive aid ATMIST, 13/79 preferred IMIST-AMBO, and 8/79 preferred MIST or SBAR as the most useful. CONCLUSIONS: There is room for improvement through standardizing communication and streamlined pre-alert channels. Some disagreements exist between TTLs, particularly regarding logistics. Further research should examine TTL satisfaction after implementing the change in the pre-alert notification framework, which can address localized issues through stakeholder meetings with individual TTLs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".