MechaScreener: Large language model-based automated screening for systematic reviews and research.

Authors

Connor Forbes, Matt Carter, Carly Hudson, Paul Glasziou, Justin Clark

Published in

Research synthesis methods. Pages 1-10. Oct 15, 2026. Epub Oct 15, 2026.

Abstract

Systematic reviews are the gold standard for evidence synthesis, but the manual title and abstract screening of thousands of references creates a severe bottleneck. Existing automated tools have historically struggled to achieve the near-perfect recall (sensitivity) required for reliable reviews. We developed MechaScreener as a 'zero-shot' automated screening tool that utilises a large language model to rank article relevance. The tool requires no initial training data or manual pre-screening, as MechaScreener directly applies user-provided question elements (PICO) or inclusion/exclusion criteria to assign an ordinal relevance score (1-5) to each reference. We evaluated the tool in two phases: a development phase using five reference libraries to optimise prompts, and an independent evaluation phase using 10 diverse Cochrane review libraries (comprising both randomised controlled trials [RCTs] and non-RCTs) containing over 58,000 references. In the evaluation dataset, MechaScreener achieved a perfect recall of 1.00 (100%) in all 10 reviews, ensuring that no relevant articles were missed (pooled record-level 95% CI: 0.98-1.00). Concurrently, it achieved an overall macro-average specificity of 0.61 (61%, cluster bootstrap 95% CI: 0.46-0.75; pooled record-level specificity 0.59, 95% CI: 0.59-0.60). Specificity varied from 0.21 in broad public health topics to 0.91 in precise pharmacological interventions, reflecting the tool's built-in conservatism when evaluating ambiguous abstracts. By safely eliminating over 60% of irrelevant literature during the initial screening phase without compromising recall, MechaScreener functions as a highly reliable but low-effort 'first-pass' filter, allowing researchers to substantially reduce manual workloads and reallocate resources towards full-text review and data extraction.

PMID:
42876407
Bibliographic data and abstract were imported from PubMed on 15 Oct 2026.

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