A Modified Delphi Study to Improve Prehospital Mass Casualty Incident Response
Bibliographic record
Abstract
Introduction: The Novel Integrated Toolkit for Enhanced Pre-Hospital Life Support and Triage in Challenging and Large Emergencies (NIGHTINGALE) project was awarded to a consortium to design an innovative toolkit featuring different technological solutions for prehospital mass casualty incident (MCI) response. Translational science (T) methodology was undertaken to develop evidence-based guidelines for MCI response. Method: The consortium was divided into three work groups (WGs) MCI Triage, Prehospital Life Support and Damage Control and Prehospital Processes. Each WG previously collected data through the project T1 scoping review stage to provide the foundation for the initial T2 modified Delphi draft statements to present to WG internal focus groups for content and NIGHTINGALE study objectives. Their refined statements proceeded to WG specific external focus groups for further editing to be clear and concise for the following modified Delphi consensus rounds. Final WG statements were presented to modified Delphi experts for their consensus using the STAT59 platform with instruction to rank each statement on a seven-point linear numeric scale, where 1 = disagree and 7 = agree. Consensus amongst experts was defined as a standard deviation ≤1.0. Results: After three modified Delphi rounds, 18 of 24 statements attained consensus by the MCI Triage experts, eight of 25 by the Prehospital and Life Support and Damage Control experts, and 23 of 28 by the Prehospital Processes experts. Conclusion: The three work groups will utilize consensus statements during the NIGHTINGALE project T3 phase to create evidence-based MCI response 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 | 0.184 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".