Authors
Edwina Mead, Darren Rajit, Vanessa Scarf, Emily Callander
Published in
Annals of epidemiology. Pages 110319. Nov 14, 2026. Epub Nov 14, 2026.
Abstract
Rapid growth in medical literature limits traditional systematic review methods, particularly in high-volume fields like maternity care. This paper presents a novel methodological approach using a hybrid workflow that combines Large Language Model (LLM) semantic data extraction with programmatic filtering to automate title and abstract screening.
The workflow was deployed on a high-volume clinical dataset (51,281 records retrieved from six databases) as a validation case study. Google Gemini 1.5-pro and 2.5-flash extracted 12 structured semantic variables, followed by a seven-stage rule-based filtering pipeline to determine eligibility. The workflow's accuracy was validated against a large-scale manual audit.
The workflow reduced the human manual screening burden by 96%. Validation demonstrated an estimated sensitivity of 89.9% (95% confidence interval; CI 87.3-92.1%), specificity of 96.4% (95% CI 96.2-96.5%), and false negative rate of 0.18% (95% CI 0.09-0.35%), achieving accuracy comparable to dual-human review. Total API expenditure was US$458.73. Subgroup analysis found no change in screening accuracy after changing LLM.
This approach offers a scalable, transparent, cost-effective, and model-agnostic solution for evidence synthesis. By decoupling data extraction from inclusion logic, researchers maintain granular control over eligibility criteria, supporting scalable evidence synthesis and living systematic reviews.
PMID:
43033879
Bibliographic data and abstract were imported from PubMed on 15 Nov 2026.
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