A Delphi study to identify prehospital and emergency department trauma care modifiers for older adults
Bibliographic record
Abstract
Background: Older patients (age ≥ 65 yr) with trauma have increased morbidity and mortality compared to younger patients; this is partly explained by undertriage of older patients with trauma, resulting in lack of transfer to a trauma centre or failure to activate the trauma team. The objective of this study was to identify modifiers to the prehospital and emergency department phases of major trauma care for older adults based on expert consensus. Methods: We conducted a modified Delphi study between May and September 2019 to identify major trauma care modifiers for older adults based on national expert consensus. The panel consisted of 24 trauma care professionals from across Canada from the prehospital and emergency department phases of care. The survey consisted of 16 trauma care modifiers. Three online survey rounds were distributed. Consensus was defined a priori as a disagreement index score less than 1. Results: There was a 100% response rate for all survey rounds. Three new trauma care modifiers were suggested by panellists. The panel achieved consensus agreement for 17 of the 19 trauma care modifiers. The prehospital modifier with the strongest agreement to transfer to a trauma centre was a respiratory rate less than 10 or greater than 20 breaths/min or need for ventilatory support. The emergency department modifier with the strongest level of agreement was obtaining 12-lead electrocardiography following the primary and secondary survey. Conclusion: Using a modified Delphi process, an expert panel agreed on 17 trauma care modifiers for older adults in the prehospital and emergency department settings. These modifiers may improve the delivery of trauma care for older adults and should be considered when developing local and national trauma guidelines.
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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 | 0.062 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".