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- W4386979600 abstract "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." @default.
- W4386979600 created "2023-09-24" @default.
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- W4386979600 date "2023-09-23" @default.
- W4386979600 modified "2023-10-08" @default.
- W4386979600 title "Semi-automating abstract screening with a natural language model pretrained on biomedical literature" @default.
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- W4386979600 doi "https://doi.org/10.1186/s13643-023-02353-8" @default.
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