Evidence map›Paper›PMID 28269965›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2016

Term Coverage of Dietary Supplements Ingredients in Product Labels.

Yefeng Wang, Terrence J Adam, Rui Zhang

Open access · greenAbstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.5field-weighted citation impact, top 14% of its field
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

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.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. Article
  3. Assessing the enrichment of dietary supplement coverage in the Unified Medical Language System.Journal of the American Medical Informatics Association : JAMIA · 2020
    Article
  4. iDISK: the integrated DIetary Supplements Knowledge base.Journal of the American Medical Informatics Association : JAMIA · 2020
    Article
  5. Normalizing Dietary Supplement Product Names Using the RxNorm Model.Studies in health technology and informatics · 2019
    Article
  6. Identifying natural health product and dietary supplement information within adverse event reporting systems.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018
    Article
  7. Identifying Supplement Use Within Clinical Notes: An Applicationof Natural Language Processing.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018
    Article
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

3 authors at 1 institution in 1 country.

Yefeng WangInstitute for Health Informatics.
Terrence J AdamInstitute for Health Informatics; College of Pharmacy.
Rui ZhangInstitute for Health Informatics; Department of Surgery; University of Minnesota, Minneapolis, MN.
Institute for Medical Informatics and Biostatistics · CH

Funding

University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UL1TR000114 · NCATS · UNIVERSITY OF MINNESOTA · PI BLAZAR, BRUCE R · 2012 to 2015
$35.0M
University of Minnesota Clinical and Translational Science Institute (UMN CTSI)KL2TR000113 · NCATS · UNIVERSITY OF MINNESOTA · PI BLAZAR, BRUCE R · 2012 to 2015
$5.4M
NCATS NIH HHS KL2 TR000113NCATS NIH HHS UL1 TR000114
6 · The paper itself

Abstract

As the clinical application and consumption of dietary supplements has grown, their side effects and possible interactions with prescribed medications has become a serious issue. Information extraction of dietary supplement related information is a critical need to support dietary supplement research. However, there currently is not an existing terminology for dietary supplements, placing a barrier for informatics research in this field. The terms related to dietary supplement ingredients should be collected and normalized before a terminology can be established to facilitate convenient search on safety information and control possible adverse effects of dietary supplements. In this study, the Dietary Supplement Label Database (DSLD) was chosen as the data source from which the ingredient information was extracted and normalized. The distribution based on the product type and the ingredient type of the dietary supplements were analyzed. The ingredient terms were then mapped to the existing terminologies, including UMLS, RxNorm and NDF-RT by using MetaMap and RxMix. The large gap between existing terminologies and ingredients were found: only 14.67%, 19.65%, and 12.88% of ingredient terms were covered by UMLS, RxNorm and NDF-RT, respectively.

Indexed as

Dietary SupplementsProduct LabelingVocabulary, ControlledDatabases, FactualDrug LabelingRxNormUnified Medical Language System

Identifiers

PMID28269965
PMCPMC5333301
OpenAlexW2612107143

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.