Evidence map›Paper›PMID 24198964›Full record

ArticleCardiovascular psychiatry and neurology2013

Combining personality traits with traditional risk factors for coronary stenosis: an artificial neural networks solution in patients with computed tomography detected coronary artery disease.

Angelo Compare, Enzo Grossi, Massimo Buscema, Cristina Zarbo, Xia Mao, Francesco Faletra, Elena Pasotti, Tiziano Moccetti, Paula M C Mommersteeg, Angelo Auricchio

Open access · hybridAbstract read
In one paragraph

Article in Cardiovascular psychiatry and neurology, 2013. 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
1.0field-weighted citation impact, top 20% 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

2 citing papers in PubMed, 27 citations in OpenAlex.

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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

10 authors at 5 institutions in 5 countries.

Angelo CompareUniversity of Bergamo, Piazzale S. Agostino 2, P.O. Box 24129, Bergamo, Italy.
Enzo Grossi
Massimo Buscema
Cristina Zarbo
Xia Mao
Francesco Faletra
Elena Pasotti
Tiziano Moccetti
Paula M C Mommersteeg
Angelo Auricchio
Epatocentro Ticino · CHUniversity of Bergamo · ITBeihang University · CNSemeion Centro Ricerche di Scienze della Comunicazione · ITTilburg University · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background. Coronary artery disease (CAD) is a complex, multifactorial disease in which personality seems to play a role but with no definition in combination with other risk factors. Objective. To explore the nonlinear and simultaneous pathways between traditional and personality traits risk factors and coronary stenosis by Artificial Neural Networks (ANN) data mining analysis. Method. Seventy-five subjects were examined for traditional cardiac risk factors and personality traits. Analyses were based on a new data mining method using a particular artificial adaptive system, the autocontractive map (AutoCM). Results. Several traditional Cardiovascular Risk Factors (CRF) present significant relations with coronary artery plaque (CAP) presence or severity. Moreover, anger turns out to be the main factor of personality for CAP in connection with numbers of traditional risk factors. Hidden connection map showed that anger, hostility, and the Type D personality subscale social inhibition are the core factors related to the traditional cardiovascular risk factors (CRF) specifically by hypertension. Discussion. This study shows a nonlinear and simultaneous pathway between traditional risk factors and personality traits associated with coronary stenosis in CAD patients without history of cardiovascular disease. In particular, anger seems to be the main personality factor for CAP in addition to traditional risk factors.

Identifiers

PMID24198964
PMCPMC3808723
OpenAlexW2166373226

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LicenceCC BY
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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.