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Record W2418012119 · doi:10.1097/mco.0000000000000216

Body composition phenotypes and obesity paradox

2015· review· en· W2418012119 on OpenAlexaff
Carla M. Prado, Marı́a Cristina González, Steven B. Heymsfield

Bibliographic record

VenueCurrent Opinion in Clinical Nutrition & Metabolic Care · 2015
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Alberta
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsObesity paradoxObesityAdipose tissueConfoundingBody mass indexComposition (language)Lean body massMedicineClassification of obesityBioinformaticsFat massOverweightInternal medicineBiologyBody weight

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The obesity paradox is a highly controversial concept that may be attributed to methodological limitations related to its identification. One of the primary concerns is the use of BMI to define obesity. This index does not differentiate lean versus adipose tissue compartments (i.e. body composition) confounding health consequences for morbidity and mortality, especially in clinical populations. This review will describe the past year's evidence on the obesity paradox phenomenon, primarily focusing on the role of abnormal body composition phenotypes in explaining the controversies observed in the literature. RECENT FINDINGS: In spite of the substantial number of articles investigating the obesity paradox phenomenon, less than 10% used a direct measure of body composition and when included, it was not fully explored (only adipose tissue compartment evaluated). When lean tissue or muscle mass is taken into account, the general finding is that a high BMI has no protective effect in the presence of low muscle mass and that it is the latter that associates with poor prognosis. SUMMARY: In view of the body composition variability of patients with identical BMI, it is unreasonable to rely solely on this index to identify obesity. The consequences of a potential insubstantial obesity paradox are mixed messages related to patient-related prognostication.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.916
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.289
GPT teacher head0.542
Teacher spread0.253 · 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.

Study designNot applicable
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

Citations157
Published2015
Admission routes1
Has abstractyes

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