A Study on Agreement in PICO Span Annotations
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Abstract (arXiv)
In evidence-based medicine, relevance of medical literature is determined by predefined relevance conditions. The conditions are defined based on PICO elements, namely, Patient, Intervention, Comparator, and Outcome. Hence, PICO annotations in medical literature are essential for automatic relevant document filtering. However, defining boundaries of text spans for PICO elements is not straightforward. In this paper, we study the agreement of PICO annotations made by multiple human annotators, including both experts and non-experts. Agreements are estimated by a standard span agreement (i.e., matching both labels and boundaries of text spans), and two types of relaxed span agreement (i.e., matching labels without guaranteeing matching boundaries of spans). Based on the analysis, we report two observations: (i) Boundaries of PICO span annotations by individual human annotators are very diverse. (ii) Despite the disagreement in span boundaries, general areas of the span annotations are broadly agreed by annotators. Our results suggest that applying a standard agreement alone may undermine the agreement of PICO spans, and adopting both a standard and a relaxed agreements is more suitable for PICO span evaluation.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Apr 2019.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 1; 2024: 0; 2025: 0; 2026: 1.
6 papers.
- 2023Jointly Extracting Interventions, Outcomes, and Findings from RCT Reports with LLMscited by 10
- 2022Event Causality Identification with Causal News Corpus -- Shared Task 3, CASE 2022cited by 3
- 2021Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotationscited by 2
- 2022CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extractioncited by 1
- 2021Mirror Matching: Document Matching Approach in Seed-driven Document Ranking for Medical Systematic Reviewscited by 0
- 2026Who's Behind It? Annotating and Extracting Conspiratorial Actors from German Telegram Postscited by 0
- 2026Who's Behind It? Annotating and Extracting Conspiratorial Actors from German Telegram Posts6 Jul 2026
- 2023Jointly Extracting Interventions, Outcomes, and Findings from RCT Reports with LLMs5 May 2023
- 2022Event Causality Identification with Causal News Corpus -- Shared Task 3, CASE 202222 Nov 2022
- 2022CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction20 Sep 2022
- 2021Mirror Matching: Document Matching Approach in Seed-driven Document Ranking for Medical Systematic Reviews28 Dec 2021
- 2021Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotations6 Sep 2021