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Article analysis:

The article is generally reliable and trustworthy as it provides evidence for its claims through data analysis from 9,805 retrospectively identified sepsis cases over a two-year period at five hospitals. The authors also provide detailed information about the performance of the TREWS machine learning-based early warning system, such as its high sensitivity (82%) and adoption rate (89%). Furthermore, they provide evidence that patients whose alert was confirmed within 3 hours had a 1.85 hour reduction in median time to first antibiotic order compared to patients whose alert was dismissed, confirmed more than 3 hours after the alert, or never addressed in the system.

However, there are some potential biases that should be noted when considering this article's trustworthiness and reliability. For example, the authors do not explore any counterarguments or consider any possible risks associated with using such systems for sepsis detection. Additionally, they do not present both sides equally when discussing provider interactions with the TREWS system; instead they focus primarily on how providers interact positively with it without exploring any potential negative interactions that may have occurred during its deployment at five hospitals over two years. Finally, there is some promotional content included in the article which could be seen as biased towards TREWS and other similar systems for sepsis detection.