A Model of Glucose Production During a Meal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it