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IMPORTANCE: The scientific community debates Generative Pre-trained Transformer (GPT)-3.5's article quality, authorship merit, originality, and ethical use in scientific writing. OBJECTIVES: Assess GPT-3.5's ability to craft the background section of critical care clinical research questions compared to medical researchers with H-indices of 22 and 13. DESIGN: Observational cross-sectional study. SETTING: Researchers from 20 countries from six continents evaluated the backgrounds. PARTICIPANTS: Researchers with a Scopus index greater than 1 were included. MAIN OUTCOMES AND MEASURES: In this study, we generated a background section of a critical care clinical research question on "acute kidney injury in sepsis" using three different methods: researcher with H-index greater than 20, researcher with H-index greater than 10, and GPT-3.5. The three background sections were presented in a blinded survey to researchers with an H-index range between 1 and 96. First, the researchers evaluated the main components of the background using a 5-point Likert scale. Second, they were asked to identify which background was written by humans only or with large language model-generated tools. RESULTS: A total of 80 researchers completed the survey. The median H-index was 3 (interquartile range, 1-7.25) and most (36%) researchers were from the Critical Care specialty. When compared with researchers with an H-index of 22 and 13, GPT-3.5 was marked high on the Likert scale ranking on main background components (median 4.5 vs. 3.82 vs. 3.6 vs. 4.5, respectively; p < 0.001). The sensitivity and specificity to detect researchers writing versus GPT-3.5 writing were poor, 22.4% and 57.6%, respectively. CONCLUSIONS AND RELEVANCE: GPT-3.5 could create background research content indistinguishable from the writing of a medical researcher. It was marked higher compared with medical researchers with an H-index of 22 and 13 in writing the background section of a critical care clinical research question.

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This study introduces a culturally sensitive educational intervention to households that use open-fire cooking methods in order to improve the acceptance and sustained use of a safer cooking stove. A wood-burning stove with a closed firebox was introduced in two villages in the highlands of Guatemala. Usage rates were measured over a seven-month period after the stoves were built. Although higher initial acceptance rates were seen in the village that received the educational intervention, households in both villages showed acceptance and sustained usage rates of the stoves. This finding supports the premise that culturally sensitive educational interventions as well as community-based programmes lead to higher acceptance of initiatives, and news of these improvements spreads through culturally accepted routes.

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BACKGROUND: The criteria for the selection of COVID-19 patients that could benefit most from ECMO organ support are yet to be defined. In this study, we evaluated the predictive performance of ECMO mortality predictive models in patients with COVID-19. We also performed a cost-benefit analysis depending on the mortality predicted probability. We conducted a retrospective cohort study in COVID-19 patients who received ECMO at two tertiary care hospitals between March 2020 to July 2021. MATERIALS AND METHODS: We evaluated the discrimination (C-statistic), calibration (Cox calibration), and accuracy of the prediction of death due to severe ARDS in V-V ECMO score (PRESERVE), the Respiratory Extracorporeal Membrane Oxygenation Survival Score (RESP) score, and the PREdiction of Survival on ECMO Therapy-Score (PRESET) score. In addition, we compared the RESP score with Plateau pressure instead of Peak pressure. RESULTS: We included a total of 36 patients, 29 (80%) of them male and with a median (IQR) APACHE of 10 (8-15). The PRESET score had the highest discrimination (AUROCs 0.81 [95%CI 0.67-0.94]) and calibration (calibration-in-the-large 0.5 [95%CI -1.4 to 0.3]; calibration slope 2.2 [95%CI 0.7/3.7]). The RESP score with Plateau pressure had higher discrimination than the conventional RESP score. The cost per QALY in the USA, adjusted to life expectancy, was higher than USD 100 000 in patients older than 45 years with a PRESET > 10. CONCLUSION: The PRESET score had the highest predictive performance and could help in the selection of patients that benefit most from this resource-demanding and highly invasive organ support.

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Background: There are limited data on unconscious bias in healthcare, but there is consistent evidence that it alters clinical decision-making. COVID-19 exacerbated many pre-existing disparities, and this paper seeks to identify, deconstruct, and propose mitigation strategies for a few of them. Discussion: Five of the largest disparities amplified by the pandemic are discussed in this paper. Older people, Black people, uninsured people, rural communities, and people with lower education levels have been disproportionally affected in both morbidity and mortality. Conclusions: The disparities discussed above did not occur in a vacuum but are the result of systemic issues. Equity starts with understanding and addressing the root cause, and it can be worked toward with practical and impactful solutions.

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