创伤性脑损伤
医学
重症监护医学
价值(数学)
精神科
计算机科学
机器学习
作者
Stuart J McDonald,Terence J. O’Brien,Sandy R. Shultz
标识
DOI:10.1016/s1474-4422(22)00306-4
摘要
Traumatic brain injury is a heterogeneous condition, with functional outcomes that can be highly variable among patients who have seemingly similar preinjury characteristics (eg, demographics and comorbidities) and injury factors (eg, mechanism and extent of brain damage). The analysis of large databases led to development of the commonly used IMPACT and CRASH prognostic models, which incorporate variables such as age, Glasgow Coma Scale (GCS) motor score, and pupillary reactivity.1 Although these models have some ability to quantify risk of poor functional outcomes in patients with moderate or severe traumatic brain injury, identification of additional predictive factors is needed to improve model accuracy and better inform clinical practice for individual patients.
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