ArticleCampbell systematic reviews2020
On the use of computer-assistance to facilitate systematic mapping.
Article in Campbell systematic reviews, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Global Research on Pandemics or Epidemics and Mental Health: A Natural Language Processing Study.Journal of epidemiology and global health · 2024Pooled it
- Evidence-domain translation in the Lyme disease controversy over persistent post-treatment symptoms.Frontiers in cellular and infection microbiology · 2026Article
- Advocating for trust in and trustworthy AI to transform evidence synthesis.Environmental evidence · 2025Article
- Scientific literature on carbon dioxide removal revealed as much larger through AI-enhanced systematic mapping.Nature communications · 2025Article
- Poor hypotheses and research waste in biology: learning from a theory crisis in psychology.BMC biology · 2025Review
- Global Research on Natural Disasters and Human Health: a Mapping Study Using Natural Language Processing Techniques.Current environmental health reports · 2024Review
- Machine Learning Methods for Systematic Reviews:: A Rapid Scoping Review.Delaware journal of public health · 2023Article
- Existing evidence on the impacts of within-field farmland management practices on the flux of greenhouse gases from arable cropland in temperate regions: a systematic map.Environmental evidence · 2022Article
- Campbell Collaboration: Reflection on growth and cultivation from 2017 to 2021.Campbell systematic reviews · 2021Article
- Systematic mapping of global research on climate and health: a machine learning review.The Lancet. Planetary health · 2021Review
- Editorial: Types of methods research papers in the journalCampbell systematic reviews · 2021Article
- On the use of computer-assistance to facilitate systematic mapping.Campbell systematic reviews · 2020Article
- Editorial: Evidence synthesis for accelerated learning on climate solutions.Campbell systematic reviews · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The volume of published academic research is growing rapidly and this new era of "big literature" poses new challenges to evidence synthesis, pushing traditional, manual methods of evidence synthesis to their limits. New technology developments, including machine learning, are likely to provide solutions to the problem of information overload and allow scaling of systematic maps to large and even vast literatures. In this paper, we outline how systematic maps lend themselves well to automation and computer-assistance. We believe that it is a major priority to consolidate efforts to develop and validate efficient, rigorous and robust applications of these novel technologies, ensuring the challenges of big literature do not prevent the future production of systematic maps.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.