Minimal-Model Estimates of Insulin Sensitivity Are Insensitive to Errors in Glucose Effectiveness<sup>1</sup>
Bibliographic record
Abstract
The minimal-model method allows for estimation of insulin sensitivity (S(I) = P(3)/P(2)) and glucose effectiveness (S(G) = P(1)) from the time course of glucose and insulin after a glucose bolus. We previously demonstrated that the minimal-model results in overestimates of S(G) in subjects with normal insulin secretory function. To determine whether overestimation of S(G) has an impact on estimation of S(I), we examined model estimation of S(I) when S(G) was constrained to levels below that found by the regular minimal-model fit. Fifty-six glucose tolerance tests from lean and obese women, with and without polycystic ovary syndrome, were used. S(I) ranged from 0.2-22.6 x 10(-4) min(-1)/(microU/mL), and S(G) ranged from 0.8-3.8 x 10(-2) min(-1) for the standard minimal-model fits. Constraining S(G) to as low as 40% of the unconstrained value resulted in a 4-fold increase in P2 and P3, but only a 3% reduction in S(I). We conclude that estimation of the insulin sensitivity index is independent of errors in minimal-model-derived estimates of glucose effectiveness.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".