ArticleBMC medical research methodology2026
Automated tools for evidence quality assessment: a scoping review.
Article in BMC medical research methodology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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Who cites it
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Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundEvidence quality assessment is critical for informed public health decision-making, but manual approaches are time-consuming and subject to variability. Automated support tools have been proposed to improve efficiency and consistency, yet their current status has not been comprehensively or systematically mapped.
objectiveTo identify or map the characteristics, performance, and limitations of existing automated tools for evidence quality assessment.
methodsFollowing the JBI methodology and PRISMA-ScR checklist, we searched 6 English and 4 Chinese databases from their inception to February 9, 2025, to identify studies evaluating automated tools for evidence quality assessment. Eligible studies included original research on tool development, application, or validation. Study characteristics (e.g., year, country, design, tool type, technical features, reliability, and validity) were extracted and summarized descriptively.
resultsTwenty studies were included, most from the United Kingdom (30%), Canada (25%), and Australia (15%). Observational designs predominated (75%), with only 10% randomized controlled trials (RCTs). Twelve distinct tools were identified, of which 65% were publicly available. 58% of the tools were developed for RCTs, while 50% remained experimental and required human oversight. Reported outcomes focused on sensitivity, specificity, precision, efficiency, and consistency. Despite promising results, external validity and scalability were limited.
conclusionAutomated tools for evidence quality assessment show potential to enhance efficiency and consistency but remain restricted in applicability. Current tools are often tailored to clinical trials and require human supervision. Broader adaptation and rigorous validation are needed before such tools can be widely integrated into public health decision-making.
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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.