@article{ref1,
title="Injury scoring from ISS to machine learning - the rest of the story",
journal="Injury",
year="2021",
author="Osler, Turner and Hosmer, David",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="Foreseeing the outcome for injured patients has always been a concern of physicians, patients, and patient's families. The 20th century saw outcome prediction evolve from simply a guess to a mathematically defined probability of dying. Typically, this is done by using the information contained in past experience with many patients to create a mathematical model that estimates the future outcome for any given future patient. Of course, many different models can be created, so the process of finding the most accurate and yet parsimonious model requires data, guesswork, some mathematical intuition, and a fair bit of luck. It's important to keep our expectations modest, however. As the eminent statistician George Box observed: &quot;All models are wrong, but some are useful.&quot; That is, while we may find models that are accurate enough to be useful, we will never find the &quot;correct&quot; model, because it does not exist. I've been privileged to work with many people over the last 30 years, trying refine injury outcome prediction. Here my coauthor David Hosmer and I summarize my personal experience with this problem. Although this recitation is in the first person, it should be understood that I'm simply the chronicler of this tale; most of the work was done by my many colleagues.<p /> <p>Language: en</p>",
language="en",
issn="0020-1383",
doi="10.1016/j.injury.2021.11.043",
url="http://dx.doi.org/10.1016/j.injury.2021.11.043"
}