Evidence map›Paper›PMID 39953273›Full record

ArticleDrug safety2025

Aggregation of Adverse Event Terms for Signal Detection and Labeling in Clinical Trials.

Richard C Zink, Rebecca Lyzinski, Geoffrey Mann

Abstract read
PubMed Publisher
In one paragraph

Article in Drug safety, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Richard C ZinkJMP Statistical Discovery LLC, 100 SAS Campus Drive, Cary, NC, 27513, USA. richard.zink@jmp.com.ORCID http://orcid.org/0000-0003-4238-9395
Rebecca LyzinskiJMP Statistical Discovery LLC, 100 SAS Campus Drive, Cary, NC, 27513, USA.
Geoffrey MannJMP Statistical Discovery LLC, 100 SAS Campus Drive, Cary, NC, 27513, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Medical Dictionary for Regulatory Activities (MedDRA) was developed in the mid-to-late 1990s to address the shortcomings of other medical dictionaries used for coding adverse events. Since that time, MedDRA has become the required coding dictionary for major regulatory authorities involved with the International Council for Harmonisation. Due to the increased specificity and significant increase in terminology over time, several approaches have been developed to aggregate terms for the purposes of signal detection and labeling. We present the approaches taken and suggested to date to aggregate preferred terms into meaningful medical concepts. We discuss the pros and cons of different methods in which to group terms, and illustrate that analyses performed for MedDRA preferred terms can also be conducted using aggregated terms. However, some features of adverse events available at the preferred term level, such as severity and relationship to study therapy, require additional consideration for analysis. In the last 25 years, the pendulum for medical coding is swinging in the other direction. Faced with a deluge of preferred terms, users of MedDRA are developing new ways in which to collapse terms into medical concepts. The ability to identify safety concerns and communicate important data in drug labels effectively and consistently are at risk, particularly with the introduction of new aggregations.

Indexed as

Adverse Drug Reaction Reporting SystemsClinical Trials as TopicDrug LabelingDrug-Related Side Effects and Adverse ReactionsTerminology as TopicHumans

Identifiers

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.