A Canadian consensus-based list of urgent and specialized in-hospital trauma care interventions to assess the accuracy of prehospital trauma triage protocols: a modified Delphi study
Bibliographic record
Abstract
BACKGROUND: Injury severity scales have traditionally been used to assess the performance of prehospital trauma triage protocols, but they correlate weakly with the urgent needs of specialized trauma care interventions. This study aimed to develop a list of in-hospital urgent and specialized trauma care interventions that require direct transport to the highest-level trauma centre within the catchment area. METHODS: Based on a list of potential participants we obtained using data on training, experience, geographic location, affiliations and role within key trauma organizations, we recruited multidisciplinary trauma experts (including prehospital, emergency, surgery and intensive care clinicians, epidemiologists and clinician/decision-makers) from across Canada to complete a 3-round modified Delphi survey. We conducted a literature review of the criteria used to define urgent and specialized trauma care, and included all diagnostic and therapeutic interventions presented in previously published studies in the list of interventions to present to the panellists. The final list was determined by our advisory committee, 5 clinicians with experience in trauma care. Participants were asked to rate their level of agreement for potentially including the 38 items as urgent and specialized trauma care interventions on a 9-point Likert scale. Interventions were retained if more than 67% of participants moderately or strongly agreed (7-9 on the Likert scale). Interventions that did not reach consensus were presented again in the subsequent round. RESULTS: Twenty-three panellists were recruited. The response rate was 91%, 96% and 83% for the 3 rounds. After the Delphi process, 30 of the 38 interventions, including endotracheal intubation, blood product administration and angioembolization, and abdominal, thoracic, neurosurgical, spinal and/or orthopedic operations (excluding hip or limb surgery, and toe or finger amputation), were selected. Hospital admission to the intensive care unit and/or for observation of brain, spinal, thoracic or abdominal injuries were also retained. CONCLUSION: We developed a Canadian consensus-based list of urgent and specialized in-hospital trauma care interventions requiring direct transportation to a major trauma centre. This list should help standardize assessments of current protocols and derive new triage tools.
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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 | 0.091 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".