Repeatability of 2 Methods for Assessment of Insulin Sensitivity and Glucose Dynamics in Horses
Bibliographic record
Abstract
Both the euglycemic-hyperinsulinemic clamp (EHC) and minimal model analysis of the frequently sampled intravenous glucose tolerance test (FSIGT) have been applied for measurement of insulin sensitivity in horses. However, no published data are available on the reproducibility of these methods. Therefore, the objective of this study was to evaluate the variation and repeatability of measures of glucose dynamics and insulin sensitivity in horses derived from minimal model analysis of the FSIGT and from the EHC method. Six healthy horses underwent both the FSIGT and EHC on 2 occasions over a 4-week period, with a minimum of 5 days between tests. Coefficient of variation (CV) and intraclass correlation coefficient (ICC) were calculated for measures of glucose metabolism and insulin sensitivity derived from each test. In the EHC, insulin sensitivity, expressed as the amount of metabolized glucose (M) per unit of serum insulin (I) (M/I ratio), averaged 0.19 +/- 0.06 x 10(-4) mmol/kg/min x (pmol/L)(-1) with an average interday CV of 14.1 +/- 5.7% (range, 7-20%) and ICC of 0.74. Minimal model analysis of the FSIGT demonstrated mean insulin sensitivity (Si) of 0.49 +/- 0.17 x 10(-4)/min x (pmol/L)(-1) with an average interday CV of 23.7 +/- 11.2% (range, 9-35%) and ICC of 0.33. Mean CV and ICC for minimal model glucose effectiveness (Sg) and acute insulin response (AIRg) were, respectively, 26.4 +/- 11.2% (range 13-40%) and 0.10 and 11.7 +/- 6.5% (range 7-21%) and 0.98. Insulin sensitivity measured by the EHC has lower interday variation when compared with the minimal model estimate derived from the FSIGT.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".