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Technical Supplement to the Medical Education Corpus

Year: 2024

Type: article

Abstract: These files comprise the technical supplement to the Medical Education Corpus (MEC), the first dedicated collection of medical education papers. Included is the technical supplement, which outlines — in brief but complete detail — all the steps taken to develop and evaluate the MEC Classifier. The MEC Classifier is a machine learning model that was used to create the MEC. There is also journal data (Medical Education Journals, consisting of MEJ-Core and MEJ-Adjacent), all labeled papers used to train the MEC Classifier, the final created MEC, along with a minimal code implementation in mec_code.py. The code file is MIT licensed. The remaining files including the dataset files and the technical supplement are licensed under CC-BY. ===== 20250324 Revision - An issue was found by Joe Costello where there were duplicate entires in Supplement 4, the MEC Papers. This issue was traced back to the code that outputted Supplement 4 as a CSV. This bug was fixed and Supplement 4 was regenerated, now without duplicates.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.14511550

Authors: Ow, Gregory, Stetson, Geoffrey, Costello, Joseph, Artino, Anthony, Maggio, Lauren

Institutions: University of California, San Francisco, University of Illinois Chicago, George Washington University

Language: English

Topic: Text and Document Classification Technologies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: green

OpenAlex ID: W6893172295

FWCI: 0.148

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Cited by: 1

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