In addition to liver function, you can't always rely on patients to have glycogen stores to draw from.
Additionally, dextrose is inexpensive, more available (rural county hospitals don't stock glucagon), easier to mix and store, has a longer shelf life, and most importantly, has a far quicker response time.
Glucagon has promise for outpatient work, where the volume of fluid is much more of a factor, though the stability and cost are still unsolved problems. The patients we treat are in a bed, monitored periodically by trained healthcare providers, with routine access to a pharmacy.
Cool - check out my company: https://replica.health - We automatically log a bunch of data for diabetics based on things like activity and location, and provide an LLM powered search engine for that data. You can ask our system questions w/ natural language like "show me data about whenever I get low post-exercise". If theres any crossover, I'd love to chat about it.
I'm mostly curious about the modelling that goes on under the hood; are you just using deep learning, or also integrating something like UVA-Padova to fit your absorption curves?
Additionally, dextrose is inexpensive, more available (rural county hospitals don't stock glucagon), easier to mix and store, has a longer shelf life, and most importantly, has a far quicker response time.
Glucagon has promise for outpatient work, where the volume of fluid is much more of a factor, though the stability and cost are still unsolved problems. The patients we treat are in a bed, monitored periodically by trained healthcare providers, with routine access to a pharmacy.
TL:DR Hospital control is just a different beast!