A Multi-faceted Analysis of Cognitive Abilities: Evaluating Prompt Methods with Large Language Models on the CONSORT Checklist

Despite the rapid expansion of Large Language Models (LLMs) in healthcare, robust and explainable evaluation of their ability to assess clinical trial reporting according to CONSORT standards remains an open challenge. In particular, uncertainty calibratio n and metacognitive reliability of LLM reasoning are poorly understood and underexplored in medical automation. This study applies a behavioral and metacognitive analytic approach using an expert - validated dataset, systematically comparing two representati ve LLMs -- one general and one domain - specialized -- across three prompt strategies. We analyze both cognitive adaptation and calibration error using metrics: Expected Calibration Error (ECE) and a baseline - normalized Relative Calibration Error (RCE) that enable s reliable cross - model comparison. Our results reveal pronounced miscalibration and overconfidence in both models, especially under clinical role - playing conditions, with calibration error persisting above clinically relevant thresholds. These findings und erscore the need for improved calibration, transparent code, and strategic prompt engineering for the development of reliable and explainable medical AI.