Evidence map›Paper›PMID 27488404›Full record

ReviewYearbook of medical informatics2016

Health Concept and Knowledge Management: Twenty-five Years of Evolution.

R Cornet, C G Chute

Abstract readHistorical ArticleReview
In one paragraph

Review in Yearbook of medical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Classification, Ontology, and Precision Medicine.The New England journal of medicine · 2018
    Review
  8. Article
  9. Article
  10. The rendering of human phenotype and rare diseases in ICD-11.Journal of inherited metabolic disease · 2018
    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

2 authors.

R CornetRonald Cornet, PhD, Visiting Associate Professor, Linköping University, Assistant Professor, Academisch Medisch Centrum, Medical Informatics, J1b-115, P.O. Box 22700, 1100 DE Amsterdam, The Netherlands, E-Mail: r.cornet@amc.uva.nl.
C G ChuteChristopher G Chute, MD DrPH, Bloomberg Distinguished Professor of Health Informatics, Professor of Medicine, Public Health, and Nursing, Chief Research Information Officer, Johns Hopkins Medicine, Johns Hopkins University, Division of General Internal Medicine, 2024 E Monument St, Suite 1-200, Baltimore, MD 21287, USA, E-Mail: chute@jhu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe fields of health terminology, classification, ontology, and related information models have evolved dramatically over the past 25 years. Our objective was to review notable trends, described emerging or enabling technologies, and highlight major terminology systems during the interval.

methodsWe review the progression in health terminology systems informed by our own experiences as part of the community involved in this work, reinforced with literature review and citation.

resultsThe transformation in size, scope, complexity, and adoption of health terminological systems and information models has been tremendous, on the scale of orders of magnitude.

conclusionThe present "big science" era of inference and discovery in biomedicine would not have been possible or scalable absent the growth and maturation of health terminology systems and information models over the past 25 years.

Indexed as

Knowledge ManagementVocabulary, ControlledClassificationHistory, 20th CenturyHistory, 21st CenturyHumansMedical Informaticsclassificationinformation modelontologyTerminology

Identifiers

PMID27488404
PMCPMC5171511

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.