bigbio/czi_drsm

Dataset Card for CZI DRSM

Research Article document classification dataset based on aspects of disease research. Currently, the dataset consists of three subsets:

(A) classifies title/abstracts of papers into most popular subtypes of clinical, basic, and translational papers (~20k papers);

- Clinical Characteristics, Disease Pathology, and Diagnosis:
    Text that describes (i) symptoms, signs, or ‘phenotype’ of a disease; 
    (ii) the effects of the disease on patient organs, tissues, or cells; 
    (iii)) the results of clinical tests that reveal pathology (including
    biomarkers); (iv) research that use this information to figure out
    a diagnosis.

- Therapeutics in the clinic: 
    Text describing how treatments work in the clinic (but not in a clinical trial).

- Disease mechanism: 

- Patient-Based Therapeutics: 
    Text describing (i) Clinical trials (studies of therapeutic measures being 
    used on patients in a clinical trial); (ii) Post Marketing Drug Surveillance 
    (effects of a drug after approval in the general population or as part of 
    ‘standard healthcare’); (iii) Drug repurposing (how a drug that has been 
    approved for one use is being applied to a new disease).

(B) identifies whether a title/abstract of a paper describes substantive research into Quality of Life (~10k papers);

- [-1] - the paper is not a primary experimental study in rare disease

- [0] - the study does not directly investigate quality of life

- [1] - the study investigates qol but not as its primary contribution

- [2] - the study's primary contribution centers on quality of life measures

(C) identifies if a paper is a natural history study (~10k papers).

• [-1] - the paper is not a primary experimental study in rare disease

- [0] - the study is not directly investigating the natural history of a disease

- [1] - the study includes some elements a natural history but not as its primary contribution

- [2] - the study's primary contribution centers on observing the time course of a rare disease

These classifications are particularly relevant in rare disease research, a field that is generally understudied.

This data was compiled through the use of a gamified curation approach based on CentaurLabs' 'diagnos.us' platform.

Citation Information

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