ArticleSystematic reviews2023
Semi-automating abstract screening with a natural language model pretrained on biomedical literature.
Article in Systematic reviews, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- The emergence of large language models as tools in literature reviews: a large language model-assisted systematic review.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- Techniques, Performance, and Feasibility of Natural Language Processing for Abstract Screening in Evidence Synthesis: A Systematic Review.Campbell systematic reviews · 2026Review
- Artificial Intelligence Resources for the Screening of Titles and Abstracts in Systematic Reviews: A Scoping Review.Cochrane evidence synthesis and methods · 2026Review
- Using full agreement across multiple large language models for title-and-abstract screening in systematic reviews: a proof-of-concept.Systematic reviews · 2026Article
- Use of Small-Molecule Inhibitors of CILK1 and AURKA as Cilia-Promoting Drugs to Decelerate Medulloblastoma Cell Replication.Biomedicines · 2026Article
- ReviewGenie: a novel automated system for systematic reviews-an exploratory study in speech and language disorders.Systematic reviews · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We demonstrate the performance and workload impact of incorporating a natural language model, pretrained on citations of biomedical literature, on a workflow of abstract screening for studies on prognostic factors in end-stage lung disease. The model was optimized on one-third of the abstracts, and model performance on the remaining abstracts was reported. Performance of the model, in terms of sensitivity, precision, F1 and inter-rater agreement, was moderate in comparison with other published models. However, incorporating it into the screening workflow, with the second reviewer screening only abstracts with conflicting decisions, translated into a 65% reduction in the number of abstracts screened by the second reviewer. Subsequent work will look at incorporating the pre-trained BERT model into screening workflows for other studies prospectively, as well as improving model performance.
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.