ArticleBioinformatics advances2023
MedicaidJS: a FAIR approach to real-time drug analytics.
Article in Bioinformatics advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed, 1 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: As prescription drug prices have drastically risen over the past decade, so has the need for real-time drug tracking resources. In spite of increased public availability to raw data sources, individual drug metrics remain concealed behind intricate nomenclature and complex data models. Some web applications, such as GoodRX, provide insight into real-time drug prices but offer limited interoperability. To overcome both obstacles we pursued the direct programmatic operation of the stateless Application Programming interfaces (HTTP REST APIs) maintained by the Food and Drug Administration (FDA), Medicaid, and National Library of Medicine. These data-intensive resources represent an opportunity to develop Software Development Kits (SDK) to streamline drug metrics without downloads or installations, in a manner that addresses the FAIR principles for stewardship in scientific data-Findability, Accessibility, Interoperability, and Reusability. These principles provide a guideline for continual stewardship of scientific data. Results: MedicaidJS SDK was developed to orchestrate API calls to three complementary data resources: Medicaid (data.medicaid.gov), Food and Drug Administration (open.fda.gov), and the National Library of Medicine RxNorm (lhncbc.nlm.nih.gov/RxNav). MedicaidJS synthesizes response data from each platform into a zero-footprint JavaScript modular library that provides data wrangling, analysis, and generation of embeddable interactive visualizations. The SDK is served on github with live examples on observableHQ notebooks. It is freely available and can be embedded into web applications as modules returning structured JSON data with standardized identifiers. Availability and implementation: Open source code publicly available at https://github.com/episphere/medicaid, live at episphere.github.io/medicaid, supplementary interactive Observable Notebooks at observablehq.com/@medicaidsdk/medicaidsdk.
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.