Extension usage examples:
Here's how our browser extension sees the article:
Article summary:
1. This study evaluated the use of a machine learning-based severe sepsis prediction system for reductions in average length of stay and in-hospital mortality rate.
2. The study was conducted at two medical-surgical intensive care units at the University of California, San Francisco Medical Center.
3. Results showed that average length of stay decreased from 13.0 days in the control to 10.3 days in the experimental group (p=0.042). In-hospital mortality decreased by 12.4 percentage points when using the MLA (p=0.018), a relative reduction of 58.0%.
Article analysis:
The article is generally trustworthy and reliable, as it provides detailed information on the methods used, results obtained, and conclusions drawn from the study. The authors have also provided a clear description of their research design and methodology, which helps to ensure that any potential biases are minimized or eliminated altogether. Furthermore, they have provided sufficient evidence to support their claims and conclusions, including statistical analysis of data collected during the trial period.
However, there are some potential sources of bias that should be noted when considering this article's trustworthiness and reliability. For example, although participants were randomly assigned to a trial arm, group assignments were automatically revealed for any patients who received MLA alerts; this could potentially introduce bias into the results if certain groups were more likely to receive alerts than others due to factors such as age or gender. Additionally, while no adverse events were reported during this trial, it is possible that some may have gone unreported due to lack of awareness or other factors; thus further research is needed to determine whether there are any long-term risks associated with using this algorithm for predicting severe sepsis in patients.