Evidence map›Paper›PMID 33139971›Full record

ArticleCEUR workshop proceedings2016

Adding evidence type representation to DIDEO.

Mathias Brochhausen, Philip E Empey, Jodi Schneider, William R Hogan, Richard D Boyce

Abstract read
PubMed Full text at PMC
In one paragraph

Article in CEUR workshop proceedings, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Mathias BrochhausenDepartment of Biomedical Infonnatics, University of Arkansas for Medical Sciences, Little Rock, AR USA.
Philip E EmpeyDepartment of Pharmacy and Therapeutics, University of Pittsburgh, Pittsburgh, PA USA.
Jodi SchneiderDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA USA.
William R HoganDepartment of Health Outcomes and Policy, University of Florida, Gainesville, FL USA.
Richard D BoyceDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA USA.

Funding

The internship in Biomedical Research, Informatics, and Computer Science (iBRIC): Biomedical Informatics and Data Science research experiences for students from Minority Serving InstitutionsT15LM007059 · NLM · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI HARRY S HOCHHEISER · 1987 to 2026
$23.9M
Addressing gaps in clinically useful evidence on drug-drug interactionsR01LM011838 · NLM · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BOYCE, RICHARD DAVID · 2014 to 2017
$1.6M
NLM NIH HHS R01 LM011838NLM NIH HHS T15 LM007059
6 · The paper itself

Abstract

In this poster we present novel development and extension of the Drug-drug Interaction and Drug-drug Interaction Evidence Ontology (DIDEO). We demonstrate how reasoning over this extension of DIDEO can a) automatically create a multi-level hierarchy of evidence types from descriptions of the underlying scientific observations and b) automatically subsume individual evidence items under the correct evidence type. Thus DIDEO will enable evidence items added manually by curators to be automatically categorized into a drug-drug interaction framework with precision and minimal effort from curators. As with all previous DIDEO development this extension is consistent with OBO Foundry principles.

Indexed as

biomedical ontologiesdrug-drug interactionevidence typespotential drug-drug interaction

Identifiers

PMID33139971
PMCPMC7603805

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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.