Evidence map›Paper›PMID 32161643›Full record

ReviewF1000Research2019

Pharmacovigilance in perspective: drug withdrawals, data mining and policy implications.

Muaed Alomar, Subish Palaian, Moawia M Al-Tabakha

Abstract readReview
In one paragraph

Review in F1000Research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

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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.

Muaed AlomarClinical Sciences Department, Ajman University, Ajman, Ajman, United Arab Emirates.ORCID 0000-0001-6526-2253
Subish PalaianClinical Sciences Department, Ajman University, Ajman, Ajman, United Arab Emirates.ORCID 0000-0002-9323-3940
Moawia M Al-TabakhaPharmaceutical Sciences Department, College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates.ORCID 0000-0001-6425-3582

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Considering that marketed drugs are not free from side effects, many countries have initiated pharmacovigilance programs. These initiatives have provided countries with methods of detection and prevention of adverse drug reactions at an earlier stage, thus preventing harm occurring in the larger population. In this review, examples of drug withdrawals due to effective pharmacovigilance programs have been provided with details. In addition, information concerning data mining in pharmacovigilance, an effective method to assess pharmacoepidemiologic data and detecting signals for rare and uncommon side effects, is also examined, which is a method synchronized with information technology and advanced electronic tools. The importance of policy framework in relation to pharmacovigilance is discussed in detail, and country experiences upon implementation of pharmacovigilance policies is highlighted.

Indexed as

Data MiningPharmacovigilanceAdverse Drug Reaction Reporting SystemsDrug-Related Side Effects and Adverse ReactionsHumansdata miningdrug withdrawalsPharmacovigilancepharmacovigilance policies

Identifiers

PMID32161643
PMCPMC7050268

What OpenQuestion holds

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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.