Making Broad Evidence Synthesis Feasible: An LLM Screening Agent for Meta-Analyses Applied To Suicide Prevention

Systematic reviews and meta-analyses inform suicide-prevention policy and practice, but broad database searches are difficult to screen manually. This limits capture of upstream interventions, such as economic policies, with indirect effects on suicide. Reliable automated screening could make broader and more comprehensive evidence syntheses feasible.

To develop and validate ScreenAgent, a large language model (LLM) agent for title and abstract screening, and a review-specific method for prospectively estimating screening performance.

ScreenAgent was validated internally on a prospective meta-analysis, and externally on two published systematic reviews. The correct include and exclude decisions followed standard systematic-review screening methodology.

ScreenAgent, an LLM agent returning structured include-or-exclude decisions. Records it marked for inclusion were re-checked by a second, cascade pass using a higher-effort LLM. For the external reviews, the agent's prompt was tuned automatically on a small set of labeled examples.

We calculated sensitivity, specificity, workload reduction (the percentage of records removed from human review), and agent-versus-human reliability via Cohen kappa. Sensitivity was estimated by direct comparison (internal) and 5-fold cross-validation (external).

In the internal validation, ScreenAgent identified 43 of 44 eligible studies (sensitivity 97.7%; 95% CI, 88.2%-99.6%) with a generic prompt applied without any review-specific optimization, specificity 98.0%, and a measured full-corpus workload reduction of 99.4%. The cost was $855.91 for the full 201,064-record corpus (0.43 US cents per record). Agent-versus-human-consensus agreement exceeded human-versus-human agreement (Cohen kappa 0.75 vs 0.64; percent agreement 97.3% vs 95.4%). For two external validation studies, automatic tuning resulted in a cross-validated sensitivity of 95.9% (95% CI, 90.0%-98.4%) and 97.4% (90.9%-99.3%), with workload reductions of 97.4% and 98.4%.

Suicide prevention efforts often require rapid consolidation of evidence because of the inherent challenges of single studies trying to prevent rare outcomes. On both internal and external validation sets, ScreenAgent identified nearly all eligible studies with human-level reliability for a fraction of a US cent per record while keeping human reviewers as the final arbiters. By making broad searches feasible and screening performance measurable beforehand, this approach can serve as a transparent methodology to strengthen the speed at which we can inform and advance suicide prevention efforts.

Question: Can an LLM agent screen records accurately to make broad-scale meta-analyses in suicide prevention more feasible?Findings: Across a prospective review and two published reviews, ScreenAgent identified nearly all eligible studies (internal sensitivity 97.7%; external 95.9/97.4%) with human-level reliability (Cohen kappa 0.75 agent-vs-consensus, vs 0.64 between humans) and 98.0% specificity.Meaning: Systematic reviews and meta-analyses are critical for evidence synthesis but are time and labor intensive. An LLM screening agent identified nearly all eligible studies with human-level reliability while reducing human workload by 99% at a fraction of a cent per record, which makes large-scale syntheses tractable.