# From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search

arXiv: https://arxiv.org/abs/2204.03096 · HTML: https://mlsanity.com/paper/2204.03096/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2022.

## Built on by (most cited first, 10 of 10)

- [Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health](https://mlsanity.com/paper/2306.10070/) (2023)
- [Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search?](https://mlsanity.com/paper/2302.03495/) (2023)
- [Zero-shot Generative Large Language Models for Systematic Review Screening Automation](https://mlsanity.com/paper/2401.06320/) (2024)
- [Neural Rankers for Effective Screening Prioritisation in Medical Systematic Review Literature Search](https://mlsanity.com/paper/2212.09017/) (2022)
- [Automated MeSH Term Suggestion for Effective Query Formulation in Systematic Reviews Literature Search](https://mlsanity.com/paper/2209.08687/) (2022)
- [Generating Natural Language Queries for More Effective Systematic Review Screening Prioritisation](https://mlsanity.com/paper/2309.05238/) (2023)
- [CSMeD: Bridging the Dataset Gap in Automated Citation Screening for Systematic Literature Reviews](https://mlsanity.com/paper/2311.12474/) (2023)
- [MeSH Suggester: A Library and System for MeSH Term Suggestion for Systematic Review Boolean Query Construction](https://mlsanity.com/paper/2212.09018/) (2022)
- [Document Set Expansion with Positive-Unlabeled Learning: A Density Estimation-based Approach](https://mlsanity.com/paper/2401.11145/) (2024)
- [A Large-Scale, Cross-Disciplinary Corpus of Systematic Reviews](https://mlsanity.com/paper/2604.22864/) (2026)

## Abstract (arXiv)

Medical systematic review query formulation is a highly complex task done by trained information specialists. Complexity comes from the reliance on lengthy Boolean queries, which express a detailed research question. To aid query formulation, information specialists use a set of exemplar documents, called `seed studies', prior to query formulation. Seed studies help verify the effectiveness of a query prior to the full assessment of retrieved studies. Beyond this use of seeds, specific IR methods can exploit seed studies for guiding both automatic query formulation and new retrieval models. One major limitation of work to date is that these methods exploit `pseudo seed studies' through retrospective use of included studies (i.e., relevance assessments). However, we show pseudo seed studies are not representative of real seed studies used by information specialists. Hence, we provide a test collection with real world seed studies used to assist with the formulation of queries. To support our collection, we provide an analysis, previously not possible, on how seed studies impact retrieval and perform several experiments using seed-study based methods to compare the effectiveness of using seed studies versus pseudo seed studies. We make our test collection and the results of all of our experiments and analysis available at http://github.com/ielab/sysrev-seed-collection