Evidence map›Paper›PMID 37860058›Full record

ArticleLearning health systems2023

Evidence Hub: A place to exchange medical knowledge and form communities.

Kenny Hong, Druvinka Bandaranayake, Guy Tsafnat

Abstract read
In one paragraph

Article in Learning health systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Kenny HongEvidentli Pty Ltd Sydney Australia.
Druvinka BandaranayakeEvidentli Pty Ltd Sydney Australia.
Guy TsafnatEvidentli Pty Ltd Sydney Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Medical knowledge is complex and constantly evolving, making it challenging to disseminate and retrieve effectively. To address these challenges, researchers are exploring the use of formal knowledge representations that can be easily interpreted by computers. Methods: Evidence Hub is a new, free, online platform that hosts computable clinical knowledge in the form of "Knowledge Objects". These objects represent various types of computer-interpretable knowledge. The platform includes features that encourage advancing medical knowledge, such as public discussion threads for civil discourse about each Knowledge Object, thus building communities of interest that can form and reach consensus on the correctness, applicability, and proper use of the object. Knowledge Objects are maintained by volunteers and published on Evidence Hub under GPL 2.0. Peer review and quality assurance are provided by volunteers. Results: Users can explore Evidence Hub and participate in discussions using a web browser. An application programming interface allows applications to register themselves as handlers of specific object types and provide editing and execution capabilities for particular object types. Conclusions: By providing a platform for computable clinical knowledge and fostering discussion and collaboration, Evidence Hub improves the dissemination and use of medical knowledge.

Indexed as

clinical knowledgecomputable knowledge representationEvidence Hubknowledge objectsmedical knowledge dissemination

Identifiers

PMID37860058
PMCPMC10582213

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.