Evaluation of proposed oral disposition index measures in relation to the actual disposition index
Bibliographic record
Abstract
Aims While the disposition index provides a useful measure of B-cell function, its calculation requires the performance of a frequently sampled intravenous glucose tolerance test (FSIVGTT). Recently, the demonstration of a hyperbolic relationship between indices of insulin secretion and insulin sensitivity derived from the oral glucose tolerance test (OGTT) has led to the introduction of two novel OGTT-based measures of B-cell function analogous to the disposition index: (i) the insulin secretion-sensitivity index-2 (ISSI-2) (defined as the ratio of the area-under-the-insulin-curve to the area-under-the-glucose curve, multiplied by the Matsuda index) and (ii) insulinogenic index (IGI)/fasting insulin. However, neither of these two measures has been directly compared with the disposition index. Methods Two hundred and thirteen non-diabetic children (122 boys, 91 girls) underwent both OGTT and FSIVGTT, allowing for the calculation of ISSI-2, IGI/fasting insulin and the disposition index. Results ISS1-2 and IGI/fasting insulin were strongly correlated with each other (r = 0.82, P < 0.0001). Both measures correlated with the disposition index, with ISSI-2 showing a modestly stronger association (ISSI-2: r = 0.24, P = 0.0003; IGI/fasting insulin: r = 0.21, P = 0.0022). Standardized linear regression analyses confirmed that the relationship between log ISSI-2 and the disposition index (standardized regression coefficient = 0.224, P = 0.001) was stronger than that between log IGI/fasting insulin and the disposition index (standardized regression coefficient = 0.166, P = 0.015). Conclusions The OGTT-derived measures ISSI-2 and IGI/fasting insulin exhibit modest correlations with the disposition index. These relationships require further assessment in other patient populations.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".