MétaCan
Menu
Back to cohort
Record W3154320441 · doi:10.3390/ijms22094339

The Role of Sarcopenic Obesity in Cancer and Cardiovascular Disease: A Synthesis of the Evidence on Pathophysiological Aspects and Clinical Implications

2021· review· en· W3154320441 on OpenAlexaff
Érika Aparecida Silveira, Rômulo Roosevelt da Silva Filho, Maria Cláudia Bernardes Spexoto, Fahimeh Haghighatdoost, Nizal Sarrafzadegan, César de Oliveira

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2021
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSarcopenic obesityMedicineDiseaseObesityCancerSarcopeniaEpidemiologyPathophysiologyBioinformaticsInternal medicineBiology

Abstract

fetched live from OpenAlex

Obesity is globally a serious public health concern and is associated with a high risk of cardiovascular disease (CVD) and various types of cancers. It is important to evaluate various types of obesity, such as visceral and sarcopenic obesity. The evidence on the associated risk of CVD, cancer and sarcopenic obesity, including pathophysiological aspects, occurrence, clinical implications and survival, needs further investigation. Sarcopenic obesity is a relatively new term. It is a clinical condition that primarily affects older adults. There are several endocrine-hormonal, metabolic and lifestyle aspects involved in the occurrence of sarcopenic obesity that affect pathophysiological aspects that, in turn, contribute to CVD and neoplasms. However, there is no available evidence on the role of sarcopenic obesity in the occurrence of CVD and cancer and its pathophysiological interplay. Therefore, this review aims to describe the pathophysiological aspects and the clinical and epidemiological evidence on the role of sarcopenic obesity related to the occurrence and mortality risk of various types of cancer and cardiovascular disease. This literature review highlights the need for further research on sarcopenic obesity to demonstrate the interrelation of these various associations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.186

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.130
GPT teacher head0.464
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations55
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Molecular SciencesSame topicNutrition and Health in AgingFrench-language works237,207