This study aimed to compare the performance of various Large Language Models (LLMs) in answering multiple-choice questions from the last six editions (2017 to 2024) of Revalida exam. The evaluation focused on models capable of processing content in Brazilian Portuguese (PT-BR), including open-source models, namely LLaMA 3.1 (8B parameters), Qwen 2.5 (7B parameters), and their reasoning-oriented distilled variants based on the DeepSeek-R1 architecture, as well as open-access commercial models such as GPT-3.5, GPT-4o, and Gemini. After evaluating the models’ accuracy against the official answer keys, GPT-4o emerged as the top-performing model, achieving an average accuracy of 63.85%. Next, GPT-4o was prompted to justify its answers to the 2024 exam, and its explanations were independently reviewed by three licensed physicians. The evaluators reported full agreement with the clinical reasoning presented, indicating the model’s ability to produce coherent and medically relevant justifications. Lastly, justifications generated by GPT-4o for correctly answered questions from previous exams (2017–2023) were compiled into a knowledge base, which was then used to enhance GPT-4o through retrieval-augmented generation and to fine-tune LLaMA 3.1, leading to measurable performance improvements on the 2024 exam. Despite promising performance, these models still demonstrate variability in responses, hallucinations, and limited reliability in high-stakes contexts. As such, their outputs should always be reviewed by qualified professionals, and human expertise remains essential in clinical decision-making and medical education scenarios, considering the PT-BR language. However, the observed gains from integrating prior exam content indicate that domain-specific adaptation strategies may help mitigate some of these limitations and enhance model alignment.

Objective To evaluate the severity of COVID-19 in cancer patients to describe clinical and epidemiological factors associated with poor outcomes (mortality and need of intensive care unit admission or mechanical ventilation). Methods Retrospective data from patients with cancer and laboratory diagnosis of COVID-19, obtained between March 16 and May 29, 2020, were retrieved out of a cancer center database. Data analyzed included patient history, age, sex, comorbidities, types of cancer and anticancer therapy. Results This sample comprised 105 patients [...] ABSTRACT Objective To evaluate the severity of COVID-19 in cancer patients to describe clinical and epidemiological factors associated with poor outcomes (mortality and need of intensive care unit admission or mechanical ventilation). Methods Retrospective data from patients with cancer and laboratory diagnosis of COVID-19, obtained between March 16 and May 29, 2020, were retrieved out of a cancer center database. Data analyzed included patient history, age, sex, comorbidities, types of cancer and anticancer therapy. Results This sample comprised 105 patients

Anticipatory nausea and vomiting are unpleasant symptoms observed before undergoing chemotherapy sessions. Less is known about the occurrence of symptoms since the advent of the new neurokinin-1 antagonist.

Highlights ■ Evaluation of a corticosteroid-free antiemetic regimen. ■ Primary endpoint: 46% nausea control. ■ Secondary endpoint: 68% emesis control. ■ Comparable to standard four-drug protocols. ABSTRACT Objective: Chemotherapy-induced nausea and vomiting are highly prevalent adverse events that can lead to poor treatment adherence and a decreased quality of life. To the best of our knowledge, the complete omission of dexamethasone from any regimen for preventing nausea and vomiting has not yet been evaluated. This study aimed to evaluate the