Introduction
Anecdotes about questionable MEDLINE indexing circulate among health/medical librarians, and the National Library of Medicine works to curate them. On the basis of external librarian feedback, ‘red flags’ no longer automatically results in the MeSH ‘Emblems and Insignia’; ‘sex assigned at birth’ no longer invokes ‘Infant’.
“As of April 2022, all journals indexed for MEDLINE are done by automated indexing, with human review and curation of results as appropriate.”
We seek to illuminate the character of this curation, as details are challenging to access. A record’s indexing can change from one day to the next, with no indication of changes other than the Modification Date-[LR] field. Search retrieval may be impacted by reindexing; the status quo of obscurity leaves users in the dark.
Method
Using the NLM e-utilities API, we downloaded every record added to PubMed over five days (n = 25,439), and continue to re-download them regularly. Scripts were implemented in R to identify records that changed method-of-indexing and isolate any MeSH added or removed. We summarize and describe this data.
Findings
1194 records changed from automated to curated (1,084; with MeSH changes in 23%) or automated to human (110; with MeSH changes in 100%).
Discussion
We present the world’s first public PubMed indexing changelog. This work is fundamental to gauging the impacts of changes-to-indexing on search results; these changes constitute data points for future research. Reindexing is metadata errata; greater accessibility and transparency empower users to engage more fully with literature platforms.

Introduction
Since April 2022, indexing of MEDLINE records is performed predominantly by algorithm, with occasional human intervention. Recent research has raised concerns about indexing algorithms’ capacity to accurately identify meaningful elements of publications. This identification is a necessary precondition to communicating them to searchers through indexing. Concerns have included the risk of the algorithm overlooking one or more relevant concepts, not making use of appropriately precise controlled vocabulary, or assigning incorrect terms due to rhetorical or technical language. The indexing algorithm from 2022-2024 (MTIA) was a rule-based system with exceptions added over time; a machine learning model (MTIX) was implemented in 2024. NLM Technical Bulletins have noted general improvements in MTIX compared to MTIA. Recent research has explored particularities of evocative language and found improvements in discerning human from nonhuman-animal subjects.
METHODS
This work will replicate and improve on a previous project that assessed overall MTIA indexing performance. Our study will assess MTIX performance by reviewing a large, recent and random sample of MTIX-indexed records to determine whether their main concepts are adequately represented.
Using a web-form displaying the journal, title and abstract of a record, our screeners will identify key concepts that, per their searching experience, would be used to retrieve it and similar records. After establishing agreement between screeners, we will compare the consensus-concepts to the indexing applied by MTIX. We will compare these findings to previous research, noting emergent trends or issues.

RESULTS & CONCLUSION
We intend to make our web-form freely available online. Other results are forthcoming.

Introduction : Literature searching is arguably a health science librarian’s bread and butter. Experienced librarians develop intuitions drawn from their searches, and subject matter knowledge accumulates from reference interviews and background readings. Collaboration with librarian colleagues, both internal and external, invites new points of view, draws on different bodies of knowledge and experience, and helps break down naturally-occurring knowledge silos. However, both of these processes - engaging with literature and searches to develop wider-ranging knowledge, and outreach to colleagues - are often personal, private, and specific to the individual librarian. Description : Drawing on recent publications proposing that health science librarians write up case reports of challenging searches, we present a particularly tricky search and the unexpected flashes of inspiration and insight that resulted in the user receiving useful articles from their librarian. Discussion : An increasing proportion of patients in Quebec, along the entire continuum of care, do not have access to a family doctor or GP (general practitioner). We describe a research project seeking to explore models of care for cancer survivors without a GP, and more broadly the particularities of searching for articles about people who do not have a doctor. We will present what seem to be the most appropriate and useful MeSH terms, as well as alternative conceptualizations of the search that added to the pool of pertinent articles retrieved; the products of an informal and fruitful collaboration across two institutions.