Objectives. Major research and implementation efforts have been devoted to indexing articles according to the major topics discussed, but much less effort to indexing their publication types and study designs (collectively, PTs). In this Perspective, we discuss how indexing PTs differs from topical MeSH indexing and requires a different approach. Materials and Methods. Rather than focus on the technical aspects of machine learning-based indexing models, we emphasize the goals and purposes for which biomedical articles are indexed, and the surprisingly thorny question of how indexing systems should be evaluated. Results. Topical Medical Subject Heading (MeSH) terms are assigned to articles that cover the major topics discussed; when more than one term is applicable, only the most specific term is assigned. In contrast, PTs are assigned to articles that have a given structure or use a particular design. To meet the needs of end-users, particularly groups involved in evidence syntheses, PT indexing needs to be comprehensive and employ probabilistic goodness-of-fit prediction scores. Whereas existing NLM hierarchies place publication types and study design-related terms on separate trees from each other, we have created a unified hierarchy that permits more appropriate retrieval via automatic expansion. Discussion. Automated PT indexing systems should allow users to input article records or full-text PDFs and receive scores in real time. This will offer consistent indexing across bibliographic databases, as well as preprints and unpublished manuscripts. Conclusions. Automated PT indexing systems, properly designed and implemented, hold the promise of greatly improving the retrieval of biomedical articles, saving substantial effort when writing evidence syntheses and benefiting other users as well.

翻译：目的： 已有大量研究與實施工作致力於根據文章探討的主要主題進行索引，但在根據其出版物類型與研究設計（統稱 PT）進行索引方面，投入的努力則少得多。在本觀點文章中，我們探討 PT 索引如何有別於主題式 MeSH 索引，並需要不同的方法。材料與方法： 我們並非專注於基於機器學習的索引模型的技術層面，而是強調生物醫學文章被索引的目標與目的，以及評估索引系統時令人驚訝的棘手問題。結果： 主題式醫學主題詞表（MeSH）詞彙被分配給涵蓋主要討論主題的文章；當適用一個以上詞彙時，僅分配最具體的詞彙。相比之下，PT 則分配給具有特定結構或使用特定設計的文章。為滿足最終使用者（特別是參與證據綜合的群體）的需求，PT 索引需要具備全面性，並使用概率性的適合度預測分數。現有的 NLM 階層架構將出版物類型與研究設計相關詞彙分置於不同的樹狀結構中，而我們則建立了一個統一的階層架構，可透過自動擴展實現更適當的檢索。討論： 自動化 PT 索引系統應允許使用者輸入文章記錄或全文 PDF，並即時接收分數。這將為各書目資料庫、以及預印本和未發表的手稿提供一致的索引。結論： 經過適當設計與實施的自動化 PT 索引系統，有望極大改善生物醫學文章的檢索，在撰寫證據綜合時節省大量努力，並惠及其他使用者。