Evidence map›Paper›PMID 31545380›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2019

Comparison of the cohort selection performance of Australian Medicines Terminology to Anatomical Therapeutic Chemical mappings.

Guan N Guo, Jitendra Jonnagaddala, Sanjay Farshid, Vojtech Huser, Christian Reich, Siaw-Teng Liaw

Open access · greenAbstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
0.2field-weighted citation impact, top 47% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

  1. Guideline
  2. Review
  3. Article
  4. Ten Topics to Get Started in Medical Informatics Research.Journal of medical Internet research · 2023
    Article
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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

6 authors at 3 institutions in 2 countries.

Guan N GuoSchool of Public Health and Community Medicine, University of New South Wales, Sydney, Australia.
Jitendra JonnagaddalaSchool of Public Health and Community Medicine, University of New South Wales, Sydney, Australia.
Sanjay FarshidSchool of Public Health and Community Medicine, University of New South Wales, Sydney, Australia.
Vojtech HuserLister Hill National Centre for Biomedical Communications, National Library of Medicine National Institutes of Health, Bethesda, Maryland, USA.
Christian ReichIQVIA, Cambridge, Massachusetts, USA.
Siaw-Teng LiawSchool of Public Health and Community Medicine, University of New South Wales, Sydney, Australia.
UNSW Sydney · AUIQVIA (United States) · USNational Institutes of Health · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveElectronic health records are increasingly utilized for observational and clinical research. Identification of cohorts using electronic health records is an important step in this process. Previous studies largely focused on the methods of cohort selection, but there is little evidence on the impact of underlying vocabularies and mappings between vocabularies used for cohort selection. We aim to compare the cohort selection performance using Australian Medicines Terminology to Anatomical Therapeutic Chemical (ATC) mappings from 2 different sources. These mappings were taken from the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) and the Pharmaceutical Benefits Scheme (PBS) schedule. MATERIALS AND

methodsWe retrieved patients from the electronic Practice Based Research Network data repository using 3 ATC classification groups (A10, N02A, N06A). The retrieved patients were further verified manually and pooled to form a reference standard which was used to assess the accuracy of mappings using precision, recall, and F measure metrics.

resultsThe OMOP-CDM mappings identified 2.6%, 15.2%, and 24.4% more drugs than the PBS mappings in the A10, N02A and N06A groups respectively. Despite this, the PBS mappings generally performed the same in cohort selection as OMOP-CDM mappings except for the N02A Opioids group, where a significantly greater number of patients were retrieved. Both mappings exhibited variable recall, but perfect precision, with all drugs found to be correctly identified.

conclusionWe found that 1 of the 3 ATC groups had a significant difference and this affected cohort selection performance. Our findings highlighted that underlying terminology mappings can greatly impact cohort selection accuracy. Clinical researchers should carefully evaluate vocabulary mapping sources including methodologies used to develop those mappings.

Indexed as

Electronic Health RecordsPatient SelectionTerminology as TopicVocabulary, ControlledAustraliaHumansInformation Storage and RetrievalPharmaceutical PreparationsSystematized Nomenclature of MedicinePharmaceutical Preparationsanatomical therapeutic chemical classificationAustralian Medicines Terminologyclinical trialscohort selectionelectronic health records

Identifiers

PMID31545380
PMCPMC7647230
OpenAlexW2976505582

What OpenQuestion holds

Textmetadata
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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.