Identifying Factors That May Influence Decision-Making Related to the Distribution of Patients During a Mass Casualty Incident
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: We aimed to identify and seek agreement on factors that may influence decision-making related to the distribution of patients during a mass casualty incident. METHODS: A qualitative thematic analysis of a literature review identified 56 unique factors related to the distribution of patients in a mass casualty incident. A modified Delphi study was conducted and used purposive sampling to identify peer reviewers that had either (1) a peer-reviewed publication within the area of disaster management or (2) disaster management experience. In round one, peer reviewers ranked the 56 factors and identified an additional 8 factors that resulted in 64 factors being ranked during the two-round Delphi study. The criteria for agreement were defined as a median score greater than or equal to 7 (on a 9-point Likert scale) and a percentage distribution of 75% or greater of ratings being in the highest tertile. RESULTS: Fifty-four disaster management peer reviewers, with hospital and prehospital practice settings most represented, assessed a total of 64 factors, of which 29 factors (45%) met the criteria for agreement. CONCLUSIONS: Agreement from this formative study suggests that certain factors are influential to decision-making related to the distribution of patients during a mass casualty incident. (Disaster Med Public Health Preparedness. 2018;12:101-108).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it