Changing multiple models into 1 model
This questions is best posed in its original domain, ie medicine, because it will require significant domain knowledge about the problem structure and the nature of the data to reason about how well a single model might do. I doubt the analogy to job applications translates perfectly here.

Right now you have one model per review topic which is able to generalize to different publications with different criteria for inclusion. You are asking whether a single model will also be able to generalize to different review topics.

Of course you could build the model and evaluate its accuracy. If that will require significant effort, then it is fair try and reason about how well you might do first, but that will only get you so far.

Again you will have to apply your own domain knowledge here, but I can at least try and give you a framework for thinking about this problem. In order to answer the main question of how well one model will generalize, you need to investigate:


Do different review topics interact with publications / inclusion criteria in similar ways?
Do any of my existing features capture the interaction between review topics and publications / inclusion criteria?
Are there any additional features I can design to capture these interactions in a general way?


To give a very simple example, if having the topic keyword x number of times in the paper was sufficient for inclusion for paper X, topic Y, publication Z with review criteria A,B,C,..., and you think this data helps you predict inclusion for a completely different paper, topic, etc. example, then you might reason that one model will generalize here.