Evidence map›Paper›PMID 32595273›Full record

ArticleBioinformatics and biology insights2020

Identifying Herbal Adverse Events From Spontaneous Reporting Systems Using Taxonomic Name Resolution Approach.

Vivekanand Sharma, Luiz Fernando Fracassi Gelin, Indra Neil Sarkar

Abstract read
In one paragraph

Article in Bioinformatics and biology insights, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Review
  6. 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

3 authors.

Vivekanand SharmaCenter for Biomedical Informatics, Brown University, Providence, RI, USA.ORCID https://orcid.org/0000-0003-1188-2312
Luiz Fernando Fracassi GelinCenter for Biomedical Informatics, Brown University, Providence, RI, USA.
Indra Neil SarkarCenter for Biomedical Informatics, Brown University, Providence, RI, USA.

Funding

Tracking and Evaluation CoreU54GM115677 · NIGMS · BROWN UNIVERSITY · PI ROUNDS, SHARON IRENE SMITH · 2016 to 2025
$45.0M
In silico identification of phyto-therapiesR01LM011963 · NLM · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI LAW, WAYNE, SARKAR, INDRA N · 2014 to 2016
$1.2M
NIGMS NIH HHS U54 GM115677NLM NIH HHS R01 LM011963
6 · The paper itself

Abstract

The efficacy and safety of herbal supplements suffer from challenges due to non-uniform representation of ingredient terms within biomedical and observational health data sources. The nature of how supplement data are reported within Spontaneous Reporting Systems (SRS) can limit analyses of supplement-associated adverse events due to the use of incorrect nomenclature or failing to identify herbs. This study aimed to extract, standardize, and summarize supplement-relevant reports from two SRSs: (1) Food and Drug Administration Adverse Event Reporting System (FAERS) and (2) Canada Vigilance Adverse Reaction (CVAR) database. A thesaurus of plant names was developed and integrated with a mapping and normalization approach that accommodated misspellings and variants. The reports gathered from FAERS between the years 2004 and 2016 show 185,915 herbal and 7,235,330 non-herbal accounting for 2.51%. The data from CVAR found 36,940 reports of herbal and 503,580 non-herbal reports between the years 1965 and 2017 for a total of 6.83%. Although not all cases were actual adverse events due to numerous variables and incomplete reporting, it is interesting to note that the herbs most frequently reported and significantly associated with adverse events were as follows:

Indexed as

Adverse Event DetectionherbalindexingSpontaneous Reporting Systems

Identifiers

PMID32595273
PMCPMC7297479

What OpenQuestion holds

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Registered trials

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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.