A Model of Glucose Production During a Meal
Bibliographic record
Abstract
The efficiency of glucose and insulin control on glucose production (EGP) plays an important role in glucose homeostasis and its derangement in diabetes. Therefore the ability to accurately quantify indices of the individual role of glucose (GE(L)) and insulin (S(I)(L)) in the suppression of EGP would allow to improve the understanding of liver metabolism. Measuring these indices by minimal modelling of tracer labelled and unlabelled glucose data is often unreliable, possibly due to an inadequate description of EGP included in the minimal model (EGP(MM)). Moreover a validation of EGP(MM) on EGP data has never been done. Here EGP(MM) and alternative EGP descriptions were tested on recent model-independent EGP data of 20 subjects obtained with a triple-tracer meal protocol. Model performances were compared in terms of data fit and physiological plausibility. EGP(MM) was not able to describe EGP data, while one of the new model showed a good fit and provided accurate and precise estimates of hepatic sensitivity indices: GE(L) = 0.013 +/- 0.001 dl/kg/min; S(I)(L) =5.71 +/- 0.48 10(-4) dl/kg/min per microU/ml (36% and 41%, respectively, of total sensitivity indices GE(TOT) and S(I)(TOT)). This novel approach will allow to enhance 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".