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Record W2092048443 · doi:10.2337/dc07-0622

Muscle and Liver Insulin Resistance Indexes Derived From the Oral Glucose Tolerance Test

2007· letter· en· W2092048443 on OpenAlexaff
Jean‐Philippe Bastard, May Faraj, Antony D. Karelis, Jennifer Lavasseur, Dominique R. Garrel, Denis Prud’homme, Rémi Rabasa‐Lhoret

Bibliographic record

VenueDiabetes Care · 2007
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversity of OttawaUniversité de Montréal
Fundersnot available
KeywordsMedicineInsulin resistanceDiabetes mellitusInternal medicineGlucose tolerance testEndocrinologyInsulin

Abstract

fetched live from OpenAlex

It was recently reported (1) that it is important to assess both hepatic and muscle insulin resistance because doing so can lead to different treatment approaches according to the insulin resistance status of each organ. Currently, the hyperinsulinemic-euglycemic clamp is the gold standard for the measurement of liver and muscle insulin sensitivity (2). However, this method is unpractical in clinical practice. For this reason, Abdul-Ghani et al. (1) developed several formulas that were derived from an oral glucose tolerance test (OGTT) for the assessment of …

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.648
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0010.001
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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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
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

Citations42
Published2007
Admission routes1
Has abstractyes

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