{
  "abstract": "Title and abstract screening is among the most labor-intensive steps of systematic reviews. Jev, a low-cost AI decision model, scores records against eligibility criteria without screening labels. We scored 169,982 records from 128 reviews and, after tuning on development reviews, evaluated 23 held-out reviews once and 31 Cochrane reviews post hoc. Jev outranked the best free ranker in all 23 held-out reviews (mean AUC 0.966 vs 0.831) and came within 0.011 AUC of Claude Opus 5.5 on 1999 records at about 1/700 of its list-price cost (US$0.02/1000 records). Combined with active learning, it raised work saved at 95% recall from 0.659 to 0.806. A fixed score threshold fell below 95% recall in 3 of 28 Cochrane reviews, whereas Jev ranking with statistical stopping (post hoc) reached 95% recall in all 51 reviews, reading 86-88% of records. Label-free ranking with Jev was accurate and inexpensive, but stopping safely required screening decisions.",
  "abstract_shown": true,
  "affiliations": [
    "Indiana University School of Medicine"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "T. Jo"
    },
    {
      "name": "D. Lee"
    },
    {
      "name": "L. Lee"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cje2v5fn7ygra.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.09.26365173",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.09.26365173v1.full",
  "id": "cje2v5fn7ygra",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.09.26365173v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "1804|Meta-analysis and systematic reviews"
    ]
  ],
  "openalex": "W7221543209",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.09.26365173v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cje2v5fn7ygra.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:43Z",
      "native_id": "10.64898/2026.10.09.26365173",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.09.26365173v2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Label-free, low-cost AI ranking for systematic review screening",
  "title_text": "Label-free, low-cost AI ranking for systematic review screening",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:10Z",
  "url": "https://research.draconitic.com/paper/cje2v5fn7ygra",
  "venue": "medRxiv",
  "versions": []
}