Minimal model estimation of glucose absorption and insulin sensitivity from oral test: validation with a tracer method
Bibliographic record
Abstract
Measuring insulin sensitivity during the physiological milieu of oral glucose perturbation, e.g., a meal or an oral glucose tolerance test, would be extremely valuable but difficult since the rate of appearance of absorbed glucose is unknown. The reference method is a tracer two-step one: first, the rate of appearance of glucose (R(a meal)(ref)) is reconstructed by employing the tracer-to-tracee ratio clamp technique with two tracers and a model of non-steady-state glucose kinetics; next, this R(a meal)(ref) is used as the known input of a model describing insulin action on glucose kinetics to estimate insulin sensitivity (SI(ref)). Recently, a nontracer method based on the oral minimal model (OMM) has been proposed to estimate simultaneously the above quantities, denoted R(a meal) and SI, respectively, from plasma glucose and insulin concentrations measured after an oral glucose perturbation. This last method has obvious advantages over the tracer method, but its domain of validity has never been assessed against a reference method. It is thus important to establish whether or not the "nontracer" R(a meal) and SI compare well with the "tracer" R(a meal)(ref) and SI(ref). We do this comparison on a database of 88 subjects, and it is very satisfactory: R(a meal) profiles agree well with the R(a meal)(ref) and correlation of SI(ref) with SI is r = 0.86 (P < 0.0001). We conclude that OMM candidates as a reliable tool to measure both the rate of glucose absorption and insulin sensitivity from oral glucose tests without employing tracers.
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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 | 0.002 | 0.008 |
| 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".