Prioritising time-critical injuries and interventions for trapped motor vehicle collision patients: a Delphi study
Bibliographic record
Abstract
BACKGROUND: Physically trapped patients following motor vehicle collisions are at high risk of time-critical injuries and poor outcomes. Despite this, there is limited consensus on which injuries should be prioritised and which early interventions are both necessary and feasible in the prehospital setting. This study aims to develop expert consensus on injury categorisation and the delivery of early care interventions to guide clinical and operational decision-making at the scene. METHODS: A modified Delphi method was used to gather consensus from a multidisciplinary panel of subject matter experts, including clinicians, paramedics, and members of fire and rescue services. In Round 1, participants contributed to the development of draft statements relating to injury time sensitivity, intervention prioritisation, and responder roles. In Rounds 2 and 3, participants rated their level of agreement with these refined statements. A final face-to-face consensus meeting was held to discuss statements that had not yet reached consensus, explore areas of disagreement, and conduct further voting where appropriate. Consensus was defined as ≥ 70% agreement. RESULTS: Consensus was achieved on 45 statements across the domains of injury categorisation, time-critical interventions, and multi-agency responsibilities. Participants strongly endorsed the early delivery of analgesia, tranexamic acid, and protection from environmental stressors, regardless of provider background, provided that appropriate training and governance are in place. There was broad support for expanding the scope of practice of non-clinical responders to meet urgent patient needs. CONCLUSIONS: This Delphi consensus provides a framework for prioritising early interventions in the care of trapped patients. It supports a patient-centred, capability-based approach to prehospital care, emphasising feasibility, urgency, and ethical responsibility. Findings should inform the development of standard operating procedures, triage tools, and training frameworks across emergency services, with further research needed to validate assessment heuristics and address barriers to implementation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".