Kubra Cingar Alpay

Using 20 standardised geriatric pharmacotherapy vignettes (fictional older adults aged 72–88 across four clinical domains, each containing three potentially inappropriate medications and one START-type omission anchored to the AGS Beers Criteria and STOPP/START version 3), this study had GPT-5.2, Claude Sonnet 4.5, and Gemini 3 Pro respond under an identical master prompt and default end-user settings, with two geriatricians blinded to model identity independently rating anonymised outputs on a 100-point rubric (output quality 0–80, critical safety-risk prioritisation 0–20), finding that Stage 1 item-level answer-key concordance was uniformly high with limited between-model discrimination while expert-rated total scores differed significantly across models (p < 0.001; Kendall's W = 0.

Using 20 standardised geriatric pharmacotherapy vignettes (fictional older adults aged 72–88 across four clinical domains, each containing three potentially inappropriate medications and one START-type omission anchored to the AGS Beers Criteria and STOPP/START version 3), this study had GPT-5.2, Claude Sonnet 4.5, and Gemini 3 Pro respond under an identical master prompt and default end-user settings, with two geriatricians blinded to model identity independently rating anonymised outputs on a 100-point rubric (output quality 0–80, critical safety-risk prioritisation 0–20), finding that Stage 1 item-level answer-key concordance was uniformly high with limited between-model discrimination while expert-rated total scores differed significantly across models (p < 0.001; Kendall's W = 0.