SynthesisBMC medical informatics and decision making2025
A comparative study of screening performance between abstrackr and GPT models: Systematic review and contextual analysis.
Synthesis in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for Evidence Synthesis of Emerging Biologics to Improve Skeletal Health in Osteogenesis Imperfecta: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Artificial Intelligence Resources for the Screening of Titles and Abstracts in Systematic Reviews: A Scoping Review.Cochrane evidence synthesis and methods · 2026Review
- Evaluating the Methodological Quality of Artificial Intelligence-Assisted Systematic Reviews: Protocol for a Mixed Methods Meta-Research Study.JMIR research protocols · 2026Article
- From Metabolomics to Function: Ranking Plant Stem Cell Metabolomes for Use in Health and Cosmetics.Biomolecules · 2026Article
- Novel Abstract Screening Algorithm Using Delphi-Inspired Large Language Model Consensus for Systematic Reviews in Psychiatry: Nouvel algorithme de sélection des résumés utilisant un consensus issu d'un grand modèle de langage inspiré de la méthode Delphi pour les revues systématiques en psychiatrie.Canadian journal of psychiatry. Revue canadienne de psychiatrie · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSystematic reviews (SRs) and rapid reviews (RRs) are critical methodologies for synthesizing existing research evidence. However, the growing volume of literature has made the process of screening studies one of the most challenging steps in conducting systematic reviews.
methodsThis systematic review aimed to compare the performance of Abstrackr and GPT models (including GPT-3.5 and GPT-4) in literature screening for systematic reviews. We identified relevant studies through comprehensive searches in PubMed, Cochrane Library, and Web of Science, focusing on those that provided key performance metrics such as recall, precision, specificity, and F1 score.
resultsGPT models demonstrated superior performance compared to Abstrackr in precision (0.51 vs. 0.21), specificity (0.84 vs. 0.71), and F1 score (0.52 vs. 0.31), reflecting a higher overall efficiency and better balance in screening. This makes GPT models particularly effective in reducing false positives during fine-screening tasks.
conclusionAbstrackr and GPT models each offer distinct advantages in literature screening. Abstrackr is more suitable for the initial screening phases, whereas GPT models excel in fine-screening tasks. To optimize the efficiency and accuracy of systematic reviews, future screening tools could integrate the strengths of both models, potentially leading to the development of hybrid systems tailored to different stages of the screening process.
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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.