Evaluating the Performance of Agreement Metrics in a Delphi Study on Chemical, Biological, Radiological and Nuclear Major Incidents Preparedness Using Classical and Machine Learning Approaches
Bibliographic record
Abstract
ABSTRACT Delphi studies in disaster medicine lack consensus on expert agreement metrics. This study examined various metrics using a Delphi study on chemical, biological, radiological, and nuclear (CBRN) preparedness in the Middle East and North Africa region. Forty international disaster medicine experts evaluated 133 items across ten CBRN Preparedness Assessment Tool themes using a 5‐point Likert scale. Agreement was measured using Kendall's W, Intraclass Correlation Coefficient, and Cohen's Kappa. Statistical and machine learning techniques compared metric performance. The overall agreement mean score was 4.91 ± 0.71, with 89.21% average agreement. Kappa emerged as the most sensitive metric in statistical and machine learning analyses, with a feature importance score of 168.32. The Kappa coefficient showed variations across CBRN PAT themes, including medical protocols, logistics, and infrastructure. The integrated statistical and machine learning approach provides a promising method for understanding expert consensus in disaster preparedness, with potential for future refinement by incorporating additional contextual factors.
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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 | 0.281 | 0.356 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.008 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".