{
  "annotation_id": "ba849854-af77-43ae-a311-3d12fae044fc",
  "date_created": "2026-02-17T05:53:15.772000Z",
  "date_modified": "2026-02-17T05:53:15.772000Z",
  "file_hash": "6194eaea3daaa58fb98d347252459c7cf646bcd27d1459819b949682b85cf50c",
  "private": false,
  "record": {
    "abstract": "Mass casualty incidents (MCIs) are a high-risk, sensitive domain with profound implications for patient and responder safety. Augmented reality has shown promise as an assistive tool for high-stress work domains and MCI triage both in the field and for pre-field training. However, the vulnerability of MCIs makes it challenging to evaluate new tools designed to enhance MCI response. In other words, profound evolutions like the integration of augmented reality into field response require thorough proof-of-concept evaluations before being launched into real-world response. This paper describes two progressive simulation strategies for augmented reality that bridge the gap between computer-based simulation and actual field response.",
    "arxiv_id": "2601.08186",
    "authors": [
      "Cassidy R. Nelson",
      "Joseph L. Gabbard",
      "Jason B. Moats",
      "Ranjana K. Mehta"
    ],
    "categories": [
      "cs.HC"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Simulations for Augmented Reality Evaluation for Mass Casualty Incident Triage",
    "url": "https://arxiv.org/abs/2601.08186",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "0eb5273f-1468-4fea-a838-fe3f215d0c25",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}