Evidence map›Paper›PMID 40364101›Full record

ArticleJournal of clinical medicine2025

Identifying Key Predictors of Sarcopenic Obesity in Italian Severely Obese Older Adults: Deep Learning Approach.

Leticia Martins Cândido, Jun-Hyun Bae, Dae Young Kim, Munkh-Erdene Bayartai, Laura Abbruzzese, Paolo Fanari, Roberta De Micheli, Gabriella Tringali, Ana Lúcia Danielewicz, Alessandro Sartorio

Abstract read
In one paragraph

Article in Journal of clinical medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Leticia Martins CândidoGraduate Program in Public Health, Federal University of Santa Catarina, Florianópolis 88040-900, SC, Brazil.ORCID 0000-0002-3564-5322
Jun-Hyun BaeInstitute of Sport Science, Seoul National University, Seoul 08826, Republic of Korea.ORCID 0000-0003-1918-9213
Dae Young KimSenior Exercise Rehabilitation Laboratory, Department of Gerokinesiology, Kyungil University, Gyeongsan 38428, Republic of Korea.
Munkh-Erdene BayartaiDepartment of Physical and Occupational Therapy, School of Nursing, Mongolian National University of Medical Sciences, Ulaanbaatar 14210, Mongolia.ORCID 0000-0003-2726-7955
Laura AbbruzzeseDivision of Auxology, Istituto Auxologico Italiano, IRCCS, 28824 Piancavallo-Verbania, Italy.
Paolo FanariDivision of Pneumology, Istituto Auxologico Italiano, IRCCS, 28824 Piancavallo-Verbania, Italy.ORCID 0000-0001-6544-8509
Roberta De MicheliExperimental Laboratory for Auxo-Endocrinological Research, Istituto Auxologico Italiano, IRCCS, 28824 Piancavallo-Verbania, Italy.
Gabriella TringaliExperimental Laboratory for Auxo-Endocrinological Research, Istituto Auxologico Italiano, IRCCS, 28824 Piancavallo-Verbania, Italy.
Ana Lúcia DanielewiczGraduate Program in Public Health, Federal University of Santa Catarina, Florianópolis 88040-900, SC, Brazil.ORCID 0000-0003-1563-0470
Alessandro SartorioExperimental Laboratory for Auxo-Endocrinological Research, Istituto Auxologico Italiano, IRCCS, 28824 Piancavallo-Verbania, Italy.ORCID 0000-0002-9620-4133

Funding

Italian Ministry of Health - ricerca corrente 08071519
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

deep learning approachkey predictorssarcopenic obesity

Identifiers

PMID40364101
PMCPMC12072425

What OpenQuestion holds

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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.