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Article summary:

1. This observational study investigated the prevalence of thirst and associated risk factors in intensive care unit (ICU) patients.

2. The study found that 69.8% of ICU patients experienced thirst, with nil per os order, surgery, high glucose levels, and greater disease severity identified as risk factors.

3. The results suggest that timely detection of thirst symptoms and identification of those at high risk by ICU nurses can ensure the implementation of effective and safe interventions.

Article analysis:

The article is generally reliable and trustworthy due to its use of a prospective descriptive design, which allows for more accurate data collection than retrospective studies. Additionally, the authors have used a variety of methods to collect data from 301 patients from 4 ICUs (medical, surgical, cardiac and emergency ICUs), including screening interviews, questionnaires and electronic medical records. Furthermore, the findings are reported according to the STROBE checklist for cross-sectional studies which ensures accuracy in reporting results.

However, there are some potential biases that should be noted when considering the trustworthiness of this article. Firstly, the sample size is relatively small compared to other studies on similar topics which could lead to inaccurate results due to lack of statistical power or generalizability issues. Additionally, there may be selection bias as only patients admitted to four specific ICUs were included in this study which may not accurately reflect all ICU patients across different settings or countries. Finally, there may be recall bias as some data was collected retrospectively through questionnaires which could lead to inaccurate results if participants do not accurately remember their experiences or responses over time.

In conclusion, while this article is generally reliable and trustworthy due to its use of a prospective descriptive design and adherence to reporting guidelines such as STROBE checklist for cross-sectional studies; potential biases should be taken into consideration when interpreting the results such as small sample size, selection bias and recall bias.