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Record W2895006340 · doi:10.1017/s0029665118001775

Sarcopenic obesity and insulin resistance: application of novel body composition models

2018· article· en· W2895006340 on OpenAlexaff
Iara Karise Santos Mendes, Eleonora Poggiogalle, B. Lee, Carla M. Prado, Gabriele Mocciaro, G. Mariniello, José Lara, Carla Lubrano, Andrea Lenzi, Lorenzo M. Donini, Mario Siervo

Bibliographic record

VenueProceedings of The Nutrition Society · 2018
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComposition (language)Action (physics)Computer scienceContent (measure theory)Sarcopenic obesityInsulin resistanceSarcopeniaObesityMedicineEndocrinologyMathematicsLinguisticsPhysicsPhilosophy

Abstract

fetched live from OpenAlex

Sarcopenic Obesity (SO) is characterized by the co-occurrence of high adiposity (HA) and low muscle mass (LM) (1) and it has been linked to insulin resistance, inflammation and increased cardio-metabolic risk This cross-sectional study investigated the association between markers of insulin sensitivity and SO defined using three novel body composition definitions: 1) body composition phenotypes; 2) truncal fat mass/appendicular skeletal mass ratio (TrFM/ASM) load-capacity; 3) fat mass/fat free mass ratio (FM/FFM) load-capacity

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.308
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations0
Published2018
Admission routes1
Has abstractyes

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