Introduction: Since April 2022, indexing in Medline has been done through automatic indexing using an algorithm. According to PubMed, quality assurance processes are in place to ensure the accuracy of the decisions, including citations with recognized ambiguities. Humans may still participate in the indexing process by reviewing and curating the results.

Objective: Despite the introduction of the MeSH term Intersex Persons in 2020, as of January 2025, only 13 articles are tagged with the term. None of these articles were indexed using automatic indexing. This paper examines the systemic exclusion of intersex-related literature within the indexing framework in Medline.

Methods: In July 2024, a search was conducted to identify citations that included intersex in the title and abstract. The search was limited to articles published from 2020 onwards and excluded the 13 articles with the Intersex Person MeSH term.

Results: A total of 500 articles were identified. After excluding animal studies and studies that were not yet indexed, 228 articles remained. A definitive decision about the indexing could not be made for 25 articles. Of the remaining 203 articles, 147 (72.4%) were indexed using automatic indexing and the remaining 56 (27.6%) involved humans. After analyzing the full-text, it was determined that 67.3% of the automatic indexed articles and 32.7% of the manually indexed articles should have been tagged with the Intersex Persons MeSH term.

Conclusion: This study reveals significant gaps in the Medline indexing process concerning intersex-related literature. These findings indicate systemic issues in both automatic and manual indexing processes, suggesting that intersex-related research is often misunderstood, misclassified and invisible.

Efficiently retrieving relevant evidence from databases is crucial for knowledge syntheses, particularly for underrepresented or hard-to-define populations. This panel explores the development, validation, and use of search filters for locating research on specific demographic groups, such as age groups, minority populations, or workers in particular settings. Through three presentations and an interactive conversation with the panelists, we will discuss how and when to use validated and unvalidated search filters to improve retrieval efficiency for evidence relating to the health of population groups. We will discuss the principles of filter design, focusing on terminology, inclusion/exclusion criteria, and balancing sensitivity and precision. Through examples of validated filters for minority populations, we will explore how to design filters to reduce irrelevant results while maintaining inclusivity for evolving terminology. We will review methods to ensure relevance and accuracy, including insider input, pilot testing, relative recall from published reviews, and flexibility in updating for different purposes or projects. Practical applications in academic, clinical, and policy contexts will be addressed, alongside challenges like evolving language, limited metadata specific to the population, and translating filters for use in different search interfaces. We will also discuss how information specialists share filters through publication and repositories. Inspired by resources such those provided by ISSG and the University of Alberta Library, we suggest options for finding, adapting, and citing search filters. By covering both theoretical foundations and practical applications, this panel aims to improve the retrieval of population-specific health evidence, offering tools to better serve diverse populations in healthcare and social research.