{
  "abstract": "Introduction Infections are a major cause of morbidity and mortality among individuals with haematological cancers, but the duration of elevated risk in long-term survivors remains uncertain. Although previous attempts to summarise the existing literature on this topic would have been hampered by the sheer volume of studies on cancer and all-cause infections, emerging artificial intelligence tools now offer the ability to streamline the screening process, allowing for broader and more comprehensive reviews.Methods and analysis This protocol follows the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols guidelines. Eligible studies will include original observational data reporting long-term (≥1 year follow-up from diagnosis) infection-related outcomes in haematological cancer survivors compared with a general or cancer-free population. Screening will be supported by ASReview, an artificial intelligence-based tool for abstract prioritisation. An internal validation step will be conducted by comparing artificial intelligence-assisted screening results with manual review performed by two independent researchers on a subset of abstracts. The primary outcomes of infection incidence and infection mortality will be summarised by type of infection, type of haematological cancer and time since cancer diagnosis. Information on anti-cancer treatments received will also be described. Data synthesis will be mostly narrative due to the broad scope of the review, though meta-analyses will be performed in cases where studies are sufficiently homogenous. Risk of bias will be assessed using the Newcastle-Ottawa Scale.Ethics and dissemination Ethical approval is not applicable to this study. The results of the review will be disseminated to clinical audiences and submitted to a peer-reviewed journal.PROSPERO registration number CRD420251047091.",
  "authors": [
    {
      "affiliations": [
        "Department of Non-Communicable Diseases Epidemiology, London School of Hygiene & Tropical Medicine, London, UK"
      ],
      "name": "William Wilson"
    },
    {
      "affiliations": [
        "Department of Non-Communicable Diseases Epidemiology, London School of Hygiene & Tropical Medicine, London, UK"
      ],
      "name": "Harriet Forbes"
    },
    {
      "affiliations": [
        "Department of Non-Communicable Diseases Epidemiology, London School of Hygiene & Tropical Medicine, London, UK"
      ],
      "name": "Matthew Hazell"
    },
    {
      "affiliations": [
        "Department of Non-Communicable Diseases Epidemiology, London School of Hygiene & Tropical Medicine, London, UK"
      ],
      "name": "Lily Hopkins"
    },
    {
      "affiliations": [
        "Wolfson Institute of Population Health, Queen Mary University of London, London, UK"
      ],
      "name": "Garth Funston"
    },
    {
      "affiliations": [
        "University College London Hospitals NHS Foundation Trust, London, UK",
        "University College London Cancer Institute, London, UK"
      ],
      "name": "Maeve O’Reilly"
    },
    {
      "affiliations": [
        "Department of Non-Communicable Diseases Epidemiology, London School of Hygiene & Tropical Medicine, London, UK"
      ],
      "name": "Krishnan Bhaskaran"
    },
    {
      "affiliations": [
        "Department of Non-Communicable Diseases Epidemiology, London School of Hygiene & Tropical Medicine, London, UK"
      ],
      "name": "Helena Carreira"
    }
  ],
  "title": "Long-term infection risks in haematological cancer survivors compared with individuals with no cancer history: protocol for a systematic review aided by artificial intelligence-based methods",
  "uid": "6d191344-0851-5d6e-9f8b-e4be02d16054"
}