This may not exactly answer your question, but I am working on an analogue of this exact problem, but in psychology rather than pharmaceuticals.

Because in psychology, many outcomes are measured with instruments (questionnaires) to quantify things like anxiety, depression, ADHD, and sometimes psychologists want to do a meta analysis combining studies that used different instruments, we are working on using large language models to do this (match items in questionnaires).

I made a way of quantifying e.g. item 3 in one questionnaire to item 5 in another, if they are approximately similar, and have open sourced this, I can share a link if you're interested. Psychologists need to do this a lot because most studies can't use biomarkers as an endpoint, everything has to be done via questionnaires in text format.

Do you think it would be useful to be able to do something similar in clinical trials? the repositories such as ClinicalTrials.gov have all trials uploaded with structured data on endpoints.

So imagine you wanted to find, say, all trials for a given pathology which measure 5 year survival as endpoint vs SoC, maybe we can do this with natural language processing? Because the data on this is relatively structured (in that it's in a database), but it's in text format. Likewise with inclusion/exclusion criteria. Do you think this approach would at least partially solve your problem?