Use of labeled oral minimal model to measure hepatic insulin sensitivity
Bibliographic record
Abstract
The ability to accurately quantify indexes of the individual role of glucose (GE(L)) and insulin (S(I)(L)) in the suppression of endogenous glucose production (EGP) would improve the understanding of liver metabolism. Measuring these indexes during an IVGTT by minimal modeling of tracer labeled and unlabeled glucose data is often unreliable, possibly due to an inadequate description of EGP included in the Minimal Model. Moreover, a validation of the assumptions of the Minimal Model on EGP data has never been done. Recently, Krudys et al. (Krudys KM, Dodds MG, Nissen SM, Vicini P. Am J Physiol Endocrinol Metab 288: E1038-E1046, 2005) have proposed a PK/PD (pharmacokinetic/pharmacodynamic) model of the EGP profile that occurs during an intravenous glucose tolerance test (IVGTT); however, this model has also not been validated. The aim of this study was thus to test the Minimal Model, the PK/PD model, and six alternative EGP descriptions on recent model-independent EGP data of 20 subjects obtained with a triple-tracer meal protocol. Model performance was compared in terms of data fit, precision of the estimated parameters, and physiological plausibility. Neither the PK/PD nor the traditional Minimal Model were able to accurately describe EGP data or provide reliable estimates of the indexes. In contrast, one of the new models performed best by showing a good fit and providing accurate and precise estimates of hepatic sensitivity indexes: GE(L) = 0.013 +/- 0.001 dl x kg(-1) x min(-1); S(I)(L) = 5.34 +/- 0.47 10(-4) dl x kg(-1) x min(-1) per microU/ml (42 and 34%, respectively, of total sensitivity indexes GE(TOT) and S(I)(TOT)). Although this model requires further validation, it has the potential to improve our understanding of the role of the liver in pathophysiological states.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".