Introduction

For knowledge synthesis projects concerning pharmaceutical interventions, the development of a comprehensive search strategy generally requires the identification of various brand names and synonyms used for a particular drug. Building a list of these terms can take a substantial amount of time and effort, and often requires the consultation of numerous thesauri and authoritative sources. To save searchers time when building their list of terms, the author has developed a Python-based tool that queries various data sources (MeSH, RXNorm, Wikidata, and PubChem) and produces a search string that can be directly input into bibliographic databases.

Description

Using an HTML form, the searcher enters a drug name and specifies which data sources they would like to query. The tool then queries the selected data sources for the drug. If there is a match it identifies and retrieves available synonyms. The retrieved terms are then combined into a single search string that can be used in bibliographic databases such as Ovid Medline or CINAHL.

Outcomes

Leveraging existing data sources, the tool can quickly generate search strings for specific drugs. These strings return a more comprehensive set of search results than single text word searches and generating the strings requires minimal effort.

Discussion

Further work is needed to determine the effectiveness of the search strings generated by the tool, especially in comparison to manually created strings. Restrictive API access policies and fees have prevented the inclusion of some authoritative sources, which may limit the tool’s effectiveness.

Introduction: The search filter ‘exp animals/ not humans.sh’ is a well-established method in knowledge synthesis used to exclude non-human studies in Ovid Medline. We previously reported on the impact of the Medical Text Indexer-Auto (MTIA) algorithm for automated assignment of MeSH terms on the utility of this filter for knowledge synthesis projects. We sought to update our reporting to account for the 2024 implementation of the new Medical Text Indexer-NeXt Generation (MTIX) algorithm, which uses a machine-learning model for MeSH term assignment.

Methods: As in the previous study, we conducted a search in Ovid Medline using the Cochrane Highly Sensitive Search Strategy. We isolated the results indexed by the automated method and specifically excluded by the non-human-studies filter in the timeframe since MTIX was implemented. We screened these results using Covidence to identify human studies.

Results: The sample demonstrated a significant improvement over our assessment of MTIA: only 1% (25/2285) of studies screened were inappropriately excluded human studies - compared to 4.2% in the MTIA assessment - and none of these were in a clinical context. Records describing both animal and human studies continue to be a common source of inappropriate exclusion.

Discussion: Our findings suggest that the filter is much less likely to inappropriately exclude human studies indexed by MTIX (records indexed beginning April 2024) than MTIA (studies indexed between 2019 and April 2024). However, we still recommend caution with the use of the human studies filter, especially for records indexed between 2019-2024.