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Record W1607859769 · doi:10.1002/oby.20674

Ectopic fat in youth: The contribution of hepatic and pancreatic fat to metabolic disturbances

2013· article· en· W1607859769 on OpenAlexafffund
Michal Cohen, Catriona Syme, M. Deforest, Greg D. Wells, Garry Detzler, Hai‐Ling Margaret Cheng, Brian W. McCrindle, Anthony J. Hanley, Jill Hamilton

Bibliographic record

VenueObesity · 2013
Typearticle
Languageen
FieldMedicine
TopicPancreatitis Pathology and Treatment
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsInternal medicineEndocrinologyInsulinMedicineInsulin resistanceObesityVisceral fatFat accumulationAnthropometryPancreas

Abstract

fetched live from OpenAlex

OBJECTIVE: To study the relationships between parameters of glucose and insulin metabolism and visceral and abdominal ectopic fat in youth. METHODS: A cross sectional study of 50 children (24 females), 8-18 years old. Anthropometrics, body composition, blood-work and visceral and ectopic fat by magnetic resonance imaging were assessed. Insulin secretion, insulin sensitivity and beta cell function were calculated from an oral glucose tolerance test. RESULTS: BMI z-scores ranged between -1.3 and 4.5. The hepatic fat fraction (HFF) ranged between 0 and 36% and pancreatic fat fraction (PFF) between 0 and 14%. Visceral fat, HFF and PFF were associated with clinical and biochemical metabolic abnormalities, and correlated with markers of insulin sensitivity (r = -0.60, P < 0.01; r = -0.64, P < 0.01; r = -0.48, P < 0.01, respectively) insulin secretion (r = 0.55, P < 0.01; r = 0.57, P < 0.01; r = 0.41, P < 0.01, respectively), and beta cell function (r = -0.49, P < 0.01; r = -0.59, P < 0.01; r = -0.39, P < 0.01, respectively). CONCLUSIONS: Accumulations of pancreatic and hepatic fat have complementary clinical consequences in youth. While visceral and hepatic fat demonstrated a dominant effect, even relatively small degrees of pancreatic fat deposition may contribute to metabolic alterations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.011
GPT teacher head0.244
Teacher spread0.233 · 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 designObservational
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

Citations30
Published2013
Admission routes2
Has abstractyes

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