{
  "abstract": "Pain is assessed differently by patients, nurses, and clinicians, yet most computational approaches assume a single ground-truth label - effectively ignoring who is doing the rating. We introduce a rater-aware, event-aligned framework that converts sparse, rater-specific pain ratings into discrete pain-change events and aligns continuous wearable physiological signals to these events, preserving rater identity throughout. Applied to multimodal wearable data collected during spine-related pain procedures, the framework identifies substantial disagreement across rater groups and provides preliminary, exploratory evidence of rater-dependent physiological differences preceding reported pain increases. These findings suggest that pain-physiology relationships may not be rater-invariant, and that aggregating assessments across raters may mask meaningful physiological patterns. A rater-aware, event-aligned perspective is therefore a promising direction for interpreting wearable data in real-world clinical pain assessment.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2606.23705",
  "author_total": 6,
  "authors": [
    {
      "name": "Saba A. Farahani"
    },
    {
      "name": "Elahe Khatibi"
    },
    {
      "name": "Thomas D. Hughes"
    },
    {
      "name": "Ariana M. Nelson"
    },
    {
      "name": "Hung Cao"
    },
    {
      "name": "Amir M. Rahmani"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/63g82bwd72gwb.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.23705",
  "fulltext_url": null,
  "id": "63g82bwd72gwb",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.23705",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "openalex",
      "3205|Emotion and Mood Recognition"
    ]
  ],
  "openalex": "W7165761782",
  "pdf_url": "https://arxiv.org/pdf/2606.23705",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-31",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/63g82bwd72gwb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:10Z",
      "native_id": "2606.23705",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.23705"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Event-Aligned Analysis of Multi-Rater Pain Assessments Using Continuous Wearable Physiology",
  "title_text": "Event-Aligned Analysis of Multi-Rater Pain Assessments Using Continuous Wearable Physiology",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:10Z",
  "url": "https://research.draconitic.com/paper/63g82bwd72gwb",
  "venue": "arXiv",
  "versions": []
}