What types of entities can each scispaCy model recognize?

Hello,

First, thank you for developing and maintaining the scispaCy package — it’s an impressive tool and a valuable contribution to the field of biomedical NLP.

I’m currently experimenting with the **en_core_sci_md** model, and I would like to better understand what types of entities it is designed to recognize. For example, when testing the following text:

"""
The patient is a 58-year-old male with a history of **type 2 diabetes** and **hypertension**.
He presents with **chest pain** and **shortness of breath** for the past two hours.
In the emergency room, a **troponin test** was ordered, which came back elevated.
An urgent **coronary angiography** was performed, and the patient was started on **aspirin** and **atorvastatin**.
He has a known **penicillin allergy**.
His smoking history is considered a major risk factor.
"""

All the words in bold were the ones that I wanted to extract as entities, but the model only extracted the following:
- patient (ENTITY)
- male (ENTITY)
- history of type 2 diabetes (ENTITY)
- hypertension (ENTITY)
- chest pain (ENTITY)
- shortness of breath (ENTITY)
- hours (ENTITY)

Could you please point me to documentation or resources that describe the entity types covered by this model, so that I can better anticipate what it can and cannot extract?

Thank you very much for your time and for your excellent work on scispaCy!