Batch Size Effects on Mid-2025 State-of-the-Art Large Language Model Performance in Automated Title and Abstract Screening.

BACKGROUND: Manual abstract screening is a primary bottleneck in evidence synthesis. Emerging evidence suggests that large language models (LLMs) can automate this task, but their performance when processing multiple references simultaneously in "batches" is uncertain. OBJECTIVES: To evaluate the classification performance of four state-of-the-art LLMs (Gemini 2.5 Pro, Gemini 2.5 Flash, GPT-5, and GPT-5 mini) in predicting reference eligibility across a wide range of batch sizes for a systematic review of randomized controlled trials. METHODS: We used a gold-standard dataset of 790 references (93 considered relevant) from a published Cochrane Review on stem cell treatment for acute myocardial infarction. Using the public APIs for each model, batches of 1 to 790 references were submitted to classify each as "Include" or "Exclude." Performance was assessed using sensitivity and specificity, with internal validation conducted through 10 repeated runs for each model-batch combination. RESULTS: Gemini 2.5 Pro was the most robust model, successfully processing the full 790-reference batch. In contrast, GPT-5 failed at batches ≥400, while GPT-5 mini and Gemini 2.5 Flash failed at the 790-reference batch. Overall, all models demonstrated strong performance within their operational ranges, with two notable exceptions: Gemini 2.5 Flash showed low initial sensitivity at batch 1, and GPT-5 mini's sensitivity degraded at higher batch sizes (from 0.88 at batch 200 to 0.48 at batch 400). At a practical batch size of 100, Gemini 2.5 Pro achieved the highest sensitivity (1.00, 95% CI 1.00-1.00), whereas GPT-5 delivered the highest specificity (0.98, 95% CI 0.98-0.98). CONCLUSION: State-of-the-art LLMs can effectively screen multiple abstracts per prompt, moving beyond inefficient single-reference processing. However, performance is model-dependent, revealing trade-offs between sensitivity and specificity. Therefore, batch size optimization and strategic model selection are important parameters for successful implementation.