| Popis: |
Background. We evaluated the benefits and risks of using the Abstrackr machine learning (ML) tool to semi-automate title-abstract screening, and explored whether Abstrackr’s predictions varied by review or study-level characteristics.Methods. For a convenient sample of 16 reviews for which adequate data were available to address our objectives (11 systematic reviews and 5 rapid reviews) we screened a 200-record training set in Abstrackr and downloaded the relevance (relevant or irrelevant) of the remaining records, as predicted by the tool. We retrospectively simulated the liberal-accelerated screening approach. We estimated the time savings and proportion missed compared with dual independent screening. For reviews with pairwise meta-analyses, we evaluated changes to the pooled effects after removing the missed studies. We explored whether the tool’s predictions varied by review and study-level. Results. Using the ML-assisted liberal-accelerated approach, we wrongly excluded 0 to 3 (0 to 14%) records that were included in the final reports, but saved a median (IQR) 26 (9, 42) hours of screening time. One missed study was included in eight pairwise meta-analyses in one systematic review. The pooled effect for just one of those meta-analyses changed considerably (from MD (95% CI) -1.53 (-2.92, -0.15) to -1.17 (-2.70, 0.36)). Of 802 records in the final reports, 87% were correctly predicted as relevant. The correctness of the predictions did not differ by review (systematic or rapid, P=0.37) or intervention type (simple or complex, P=0.47). The predictions were more often correct in reviews with multiple (89%) vs. single (83%) research questions (P=0.01), or that included only trials (95%) vs. multiple designs (86%) (P=0.003). At the study level, trials (91%), mixed methods (100%), and qualitative (93%) studies were more often correctly predicted as relevant compared with observational studies (79%) or reviews (83%) (P=0.0006). Studies at high or unclear (88%) vs. low risk of bias (80%) (P=0.039), and those published more recently (mean (SD) 2008 (7) vs. 2006 (10), P=0.02) were more often correctly predicted as relevant. Conclusion. Our screening approach saved time and may be suitable in conditions where the limited risk of missing relevant records is acceptable. As several of our findings are paradoxical, and require further study to fully understand the tasks to which ML-assisted screening is best suited. |
