Hernandez 2
In critically ill patients who are at high risk for re-intubation, is high flow nasal oxygen non-inferior to non-invasive ventilation in reducing re-intubation and post-extubation respiratory failure?
Continue reading »A compendium of critical appraisals in Intensive Care Medicine research and related specialties
In critically ill patients who are at high risk for re-intubation, is high flow nasal oxygen non-inferior to non-invasive ventilation in reducing re-intubation and post-extubation respiratory failure?
Continue reading »
Among relatives who died in the Intensive Care Unit (ICU), does a condolence letter from a clinician, compared to no condolence letter, effect grief symptoms?
Continue reading »
In patients with moderate-to-severe sciatica, does pregabalin compared to placebo reduce leg pain?
Continue reading »
In patients that survive critical illness, does a post-discharge exercise programme compared to standard post-discharge care improve physical function?
Continue reading »
What is the discriminative accuracy of physicians and nurses in predicting 6-month mortality and functional outcomes of critically ill patients?
Continue reading »
In cancer patients with septic shock does a restrictive vs. a liberal transfusion threshold reduce 28 day mortality?
Continue reading »
In patients with refractory vasodilatory shock does the addition of angiotensin II improve blood pressure compared with standard vasopressor therapy?
Continue reading »
Rather than reinvent the wheel, we are delighted to include a link to the RCEM Critical Appraisal Dictionary. The RCEM learning site provides excellent open access educational resources, including critical care appraisal modules
Continue reading »
In patients with necrotising soft tissue infection (NSTI), does the use of intravenous immunoglobulin (IVIG) compared to placebo improve patient reported physical outcomes?
Continue reading »
In statistical hypothesis testing, a type I error is the incorrect rejection of a true null hypothesis (a “false positive”), while a type II error is incorrectly retaining a false null hypothesis (a “false negative”). The more you try and avoid a Type I error (false positive), the more likely a Type II error (false negative) may happen. Researchers have found that an alpha level of 5% is a good balance between these two issues
Continue reading »