ArticleBMC medical informatics and decision making2025
Harmonizing self-reported and free text medication data: a reproducible pipeline for gerontological research.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Tau pathology and depression interact to accelerate driving decline in cognitively normal older adults.Molecular psychiatry · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundSelf-reported medication data collected as free text in gerontological and dementia research is often unstructured with inconsistent formatting. These circumstances pose a challenge for standardization and classification when preparing effective, reproducible analyses. Spelling variations, naming conventions, and reporting drug combinations can hinder mapping to standard pharmacologic vocabularies and compromise medication exposure assessments. We aimed to develop and implement a transparent, reproducible, and scalable data harmonization pipeline that ingests free-text medication records and classifies them according to American Hospital Formulary Service (AHFS) therapeutic categories.
methodsA four-phase curation pipeline processed 30,062 Research Electronic Data Capture (REDCap) medication records collected over nearly a decade of annual visits in The Driving Real-world In-Vehicle Evaluation System (DRIVES)Project. In Phase 1, the pipeline standardized medication names using deterministic and fuzzy matching techniques, incorporating Drug-Named-Entity Recognition (DER), the thefuzz Python library, and expert review. Phase 2 mapped drugs to AHFS categories via DrugBank and RxNorm. Phase 3 generated a wide-format dataset with binary class-level exposure indicators. Phase 4 involved a final quality review with auditable documentation.
resultsOut of 30,062 entries, 16,902 eligible prescription entries remained after the removal of vitamins, supplements, and over-the-counter (OTC) drugs. Of these, automated or semi-automated processes successfully standardized 94.2% of entries, with only 5.8% requiring further expert review. A total of 444 unique medications were successfully mapped to AHFS classifications. The curated dataset enables efficient integration into analytical models and supports reproducible assessment of medication exposure.
conclusionsThis pipeline addresses a key methodological challenge in clinical research by providing a reproducible, scalable solution for harmonizing unstructured medication data and enhancing its analytical utility.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.