I have firsthand experience with this, and yes, that was my last thought. I was young and foolish, and alone in the wilderness during the winter. I drank some bad water and became very sick during a blizzard, and while searching for shelter, I finally became too weak to continue. I was hypothermic, and coherent thoughts were difficult, but my last thoughts were peaceful acceptance, and appreciation of the beauty and power around me. I cocooned up and drifted off, and I'm still here somehow.
How do you control for quality variation between patients? In my experience, AI note taking tools display a clear bias against participants who are {quieter, ESL, women, ...}. How can you evaluate whether these biases show up in a medical setting?
If you're a hobbyist runner, this equation very quickly gets flipped on its head. It's very difficult to make up the calorie deficit from a 20 mile long run every week. Throw two big races a year into the mix, and maintaining weight and muscle mass is the challenge. When I trained for ultramarathons, I sometimes ran two mountain long runs back to back. That put me 5000 calories in the hole every single weekend. I used to eat an entire large pizza by myself (2000 calories) and still be hungry 30 minutes later. There is an upper limit to how fast you can digest food, and that quickly becomes the limiting factor. I was not a professional. I ran as a hobby, rarely exceeding 50 miles a week. There are thousands of others who run at this level.
That's not strictly speaking true, since most (all?) high level languages have undefined behaviors, and their behavior varies between compilers/architectures in unexpected ways. We did lose a level of fidelity. It's still smaller than the loss of fidelity from LLMs but it is there.
In a way, yes. The specific meaning of words here is important. Sex likely refers to primary sex characteristics and gender may refer to secondary sex characteristics. For example, breast cancer is not a high risk for men, but it is for trans women.