Evidence map›Paper›PMID 38758661›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2024

Toward a unified understanding of drug-drug interactions: mapping Japanese drug codes to RxNorm concepts.

Yukinobu Kawakami, Takuya Matsuda, Noriaki Hidaka, Mamoru Tanaka, Eizen Kimura

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Observational
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yukinobu KawakamiDepartment of Medical Informatics, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, 791-0295, Japan.
Takuya MatsudaDepartment of Medical Informatics, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, 791-0295, Japan.
Noriaki HidakaDivision of Pharmacy, Ehime University Hospital, Shitsukawa, Toon, Ehime, 791-0295, Japan.
Mamoru TanakaDivision of Pharmacy, Ehime University Hospital, Shitsukawa, Toon, Ehime, 791-0295, Japan.
Eizen KimuraDepartment of Medical Informatics, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, 791-0295, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesLinking information on Japanese pharmaceutical products to global knowledge bases (KBs) would enhance international collaborative research and yield valuable insights. However, public access to mappings of Japanese pharmaceutical products that use international controlled vocabularies remains limited. This study mapped YJ codes to RxNorm ingredient classes, providing new insights by comparing Japanese and international drug-drug interaction (DDI) information using a case study methodology. MATERIALS AND

methodsTables linking YJ codes to RxNorm concepts were created using the application programming interfaces of the Kyoto Encyclopedia of Genes and Genomes and the National Library of Medicine. A comparative analysis of Japanese and international DDI information was thus performed by linking to an international DDI KB.

resultsThere was limited agreement between the Japanese and international DDI severity classifications. Cross-tabulation of Japanese and international DDIs by severity showed that 213 combinations classified as serious DDIs by an international KB were missing from the Japanese DDI information. DISCUSSION: It is desirable that efforts be undertaken to standardize international criteria for DDIs to ensure consistency in the classification of their severity.

conclusionThe classification of DDI severity remains highly variable. It is imperative to augment the repository of critical DDI information, which would revalidate the utility of fostering collaborations with global KBs.

Indexed as

Drug InteractionsKnowledge BasesRxNormEast Asian PeopleHumansJapanVocabulary, Controlleddrug codesdrug-drug interactionknowledge baseRxNormterminology

Identifiers

PMID38758661
PMCPMC11187495

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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.