ArticleAlzheimer's & dementia (Amsterdam, Netherlands)
Locally deployed large language model for real-world lecanemab eligibility pre-screening.
Article in Alzheimer's & dementia (Amsterdam, Netherlands). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionThe introduction of disease-modifying Alzheimer's therapies requires complex, labor-intensive patient screening. Cloud-based large language models (LLMs) could support this task but are often unsuitable for routine care due to data protection constraints.
methodsWe evaluated an on-premises, open-weights LLM (gpt-oss-120b) for automated extraction of therapy-relevant variables for lecanemab eligibility from German memory clinic reports. In a two-stage design, LLM-based extraction prompts, a deterministic rule-based extractor, and a shared downstream rule-based classifier were optimized on a development set (
resultsThe LLM-based pipeline achieved 94% accuracy and a Cohen's kappa of 0.90 on the hold-out set, significantly surpassing the rule-based comparator (80% accuracy) and demonstrating performance comparable to human experts. DISCUSSION: A locally deployed, on-premises LLM may assist eligibility screening as a triage support tool, potentially facilitating access to novel therapies without compromising patient data privacy.
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