Evidence map›Paper›PMID 35486326›Full record

ArticlePharmaceutical medicine2022

Dispositions and Causality Assessment in Pharmacovigilance: Proposing the Dx3 Approach for Assessing Causality with Small Data Sets.

Rani Lill Anjum, Rebecca E Chandler, Elena Rocca

Abstract read
In one paragraph

Article in Pharmaceutical medicine, 2022. 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

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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. The power of small data for personalized cancer care.Asia-Pacific journal of oncology nursing · 2022
    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.

Rani Lill AnjumNorwegian University of Life Science, Ås, Norway.ORCID http://orcid.org/0000-0002-0632-444X
Rebecca E ChandlerInternational Society of Pharmacovigilance, Uppsala, Sweden.ORCID http://orcid.org/0000-0002-9334-9046
Elena RoccaDepartment of Life Sciences and Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway. elena.rocca@oslomet.no.ORCID http://orcid.org/0000-0002-5222-0238

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A new approach is proposed for assessing causality in pharmacovigilance. The Dx3 approach is designed to qualitatively evaluate three types of dispositions when assessing whether a particular medicine has or could have caused a certain adverse event. These are: the drug disposition; the pre-disposition of the patient taking the drug (vulnerability) and; the disposition of the patient-drug interaction (mutuality). Each of these three types of dispositions will represent valuable causally relevant evidence for assessing a potential signal of harm. A checklist is provided to guide the assessment of causality for both single individual case safety reports (ICSRs) and case series. Different types of causal information are ranked according to how well suited they are for establishing a disposition. Two case examples are used to demonstrate how the approach can be used in practice for assessment purposes. One aim of the approach is to offer a qualitative way to assess causality and to make the reasoning of different assessors more transparent. A second aim is to encourage the collection of more qualitatively rich patient narratives in the ICSRs. Crucially, we believe this approach can support the inclusion of the single ICSR as a valid and valuable form of evidence.

Indexed as

Drug-Related Side Effects and Adverse ReactionsPharmacovigilanceAdverse Drug Reaction Reporting SystemsCausalityDatabases, FactualHumans

Identifiers

PMID35486326
PMCPMC9217857

What OpenQuestion holds

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LicenceCC BY-NC
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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.