Evidence map›Paper›PMID 31998737›Full record

ArticleFrontiers in medicine2019

Machine Learning Increases Diagnosticity in Psychometric Evaluation of Alexithymia in Fibromyalgia.

Graziella Orrù, Angelo Gemignani, Rebecca Ciacchini, Laura Bazzichi, Ciro Conversano

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 3 pooled it
3.8field-weighted citation impact, top 6% 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

21 citing papers in PubMed, 3 syntheses or guidelines pooled it, 35 citations in OpenAlex.

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  17. Alexithymia Is Linked with a Negative Bias for Past and Current Events in Healthy Humans.International journal of environmental research and public health · 2021
    Article
  18. Article
  19. Facial Emotion Recognition Predicts Alexithymia Using Machine Learning.Computational intelligence and neuroscience · 2021
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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

5 authors at 1 institution in 1 country.

Graziella OrrùDepartment of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, Pisa, Italy.
Angelo GemignaniDepartment of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, Pisa, Italy.
Rebecca CiacchiniDepartment of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, Pisa, Italy.
Laura BazzichiRheumatology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.
Ciro ConversanoDepartment of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, Pisa, Italy.
University of Pisa · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Here, we report an investigation on the accuracy of the Toronto Alexithymia Scale, a measure to assess alexithymia, a multidimensional construct often associate to fibromyalgia. Two groups of participants, patients with fibromyalgia (

Indexed as

alexithymiachronic conditionsexperimentfibromyalgiamachine learning

Identifiers

PMID31998737
PMCPMC6970411
OpenAlexW2998802298

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.