ALTA 2022 Shared Task (PIBOSO Sentence classification)

This dataset is described in the ALTA 2022 Shared Task and associated CodaLab competition.

The goal of this task is to build automatic sentence classifiers that can map the content of biomedical abstracts into a set of pre-defined categories, which are used for Evidence-Based Medicine (EBM). EBM practitioners rely on specific criteria when judging whether a scientific article is relevant to a given question. They generally follow the PICO criterion: Population (P) (i.e., participants in a study); Intervention (I); Comparison (C) (if appropriate); and Outcome (O) (of an Intervention). Variations and extensions of this classification have been proposed, and for this task we will extend PICO by adding the classes Background (B) and Study Design (S); and including sentences that have no relevant content: Other (O). Therefore, the goal will be to classify the provided sentences according to the PIBOSO schema. Such information could be leveraged in various ways: e.g., to improve search performance; to enable structured querying with specific categories; and to aid users in more quickly making judgements against specified PICOSO criteria.