Glucose effectiveness: measurement in diabetic and nondiabetic humans
Bibliographic record
Abstract
It is well established that under the conditions of daily living, insulin secretion and insulin action determine glucose tolerance in nondiabetic humans both in the post-absorptive and post-prandial states. However, in recent years, glucose effectiveness (i.e., the ability of glucose per se to stimulate its own uptake and to suppress its own release) has also been shown to influence glucose tolerance in both diabetic and nondiabetic individuals. In states of deficient insulin action, e.g., in individuals with type 2 diabetes, glucose effectiveness assumes a greater role in determining glucose tolerance both during fasting and post-prandial conditions. A mathematical model (Minimal Model) of glucose turnover has been applied to estimate glucose effectiveness in both diabetic and nondiabetic individuals. Several investigators have demonstrated reduced glucose effectiveness in people with type 2 diabetes mellitus. However, measurements of glucose effectiveness by the traditional single compartment minimal model approach have been fraught with errors when compared to model independent estimates, especially in the diabetic population. This has led investigators to modify the parameters of the model with the incorporation of glucose tracers and the use of two-compartment model of glucose kinetics. Although this has made the indices of glucose effectiveness more robust, proper validation experiments are necessary before widespread application of these methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".