## 3 Results
### Cognitive and Error-Type Analysis  
To deepen our understanding of the model performance beyond surface-level accuracy, we conducted a detailed analysis of the errors made by the two highest-performing language models (GPT-o1 and Deepseek-R1). This analysis considered both the cognitive demands of the exam items, classified using [PERSON]'s taxonomy, and the nature of the errors, based on qualitative evaluation of the models' answer justifications.  
Among the 298 total items administered across the nine models, the distribution across Bloom's cognitive levels was as follows: Apply (125 items, 41.9%), Understand (91 items, 30.5%), Analyze (42 items, 14.1%), and Recall (40 items, 13.4%). This distribution indicates a deliberate emphasis on higher-order cognitive processes, particularly the application and comprehension of clinical knowledge, which aligns with the CONACAM exam's focus  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **MW Statistic** & _p-Value_ \\ \hline Gemini 1.5 Pro & 2415 & 0.848 \\ \hline GPT-do & 2450 & 1 \\ \hline GPT-o1 & 2485 & 0.799 \\ \hline Qwen 2.5 & 2485 & 0.855 \\ \hline Deepseek-R1 & 2520 & 0.633 \\ \hline LLaMA 3.2 450B & 2485 & 0.851 \\ \hline LLaMA 3 70B & 2555 & 0.609 \\ \hline Anthropic Haiku & 2485 & 0.853 \\ \hline Anthropic Sonnet & 2380 & 0.676 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mann–Whitney U test comparing T = 0 vs. T = 1 performance across large language models (LLMs).  
on critical thinking and reasoning in anesthesiology. The associated error rates for these categories are shown in Figure 4.  
Figure 4: Error rates of large language model responses categorized by Bloom’s cognitive levels (Recall, Understand, Apply, and Analyze). The figure highlights higher error frequencies in application and comprehension tasks, underscoring the limitations of LLMs in complex reasoning domains.  
Figure 3: Comparison of average accuracy between human examinees and large language models (LLMs) across 15 anesthesiology knowledge domains. Areas are sorted by LLM performance from highest to lowest accuracy. Red bars represent human performance, and blue bars represent LLM performance.  
To assess the qualitative nature of the model errors, we conducted a focused review of 36 incorrect responses generated by GPT-01 and Deepseek-R1. Each response was manually classified into one of three dominant error categories: (1) Reasoning-Based Errors, which reflected flawed clinical logic or unjustified conclusions; (2) Knowledge-Based Errors, stemming from factual inaccuracies or misapplications of medical knowledge; and (3) Reading Comprehension Errors, which arose from misinterpretation or omission of key information in the question prompt.  
The distribution of these error types across cognitive domains revealed several important trends. The Apply category contained the largest number of reasoning (n = 5) and knowledge (n = 10) errors, underscoring the challenge faced by these models in integrating knowledge into clinically relevant scenarios. The Understand and Recall categories exhibited a mixture of all three error types, highlighting limitations not only in inferential reasoning but also in basic information retrieval and comprehension. In contrast, Analyze tasks were exclusively associated with knowledge-based errors, likely reflecting the small sample size and inherent complexity of such high-level tasks. The detailed breakdown is summarized in Table 3.