Evidence map›Paper›PMID 27099510›Full record

ReviewJournal of multidisciplinary healthcare2016

Conceptualizing type 2 diabetes and its management.

Peter Tsasis, Jianhong Wu, Aijun An, Hannah J Wong, Xiandong An, Zhen Mei, Ted Hains

Abstract readReview
In one paragraph

Review in Journal of multidisciplinary healthcare, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

7 authors.

Peter TsasisSchool of Health Policy and Management, York University, Toronto, ON, Canada.
Jianhong WuCentre for Disease Modelling, York University, Toronto, ON, Canada.
Aijun AnDepartment of Electrical Engineering of Computer Science, York University, Toronto, ON, Canada.
Hannah J WongSchool of Health Policy and Management, York University, Toronto, ON, Canada.
Xiandong AnDepartment of Electrical Engineering of Computer Science, York University, Toronto, ON, Canada; Manifold Data Mining Inc., Toronto, ON, Canada.
Zhen MeiManifold Data Mining Inc., Toronto, ON, Canada.
Ted HainsManifold Data Mining Inc., Toronto, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Type 2 diabetes is growing worldwide due to population growth, increased rates of obesity, unhealthy diet, and physical inactivity. Risk assessment methods can effectively evaluate the risk of diabetes, and a healthy lifestyle can significantly reduce risk or prevent complications of type 2 diabetes. However, risk assessment alone has not significantly improved poor adherence to recommended medical interventions and lifestyle changes. This paper focuses on the challenge of nonadherence and posits that improving adherence requires tailoring interventions that explicitly consider the social determinants of health.

Indexed as

data mining and cluster analysisnonadherencetailored interventionstype 2 diabetes

Identifiers

PMID27099510
PMCPMC4820210

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.