Comparison among the euglycemic-hyperinsulinemic clamp, insulin-modified frequently sampled intravenous glucose tolerance test, and oral glucose tolerance test for assessment of insulin sensitivity in healthy Standardbreds
Bibliographic record
Abstract
OBJECTIVE: To compare, in horses, estimates of insulin sensitivity obtained from minimal model analysis (MMA) of a frequently sampled IV glucose tolerance test (FSIGTT) with estimates from the euglycemic-hyperinsulinemic clamp (EHC) and to evaluate the validity of surrogate estimates of insulin sensitivity derived from an oral glucose tolerance test (OGTT). ANIMALS: 18 mature Standardbreds (mean ± SD body weight, 428.9 ± 35.9 kg; mean ± SD body condition score, 4.4 ± 1.0 [on a scale of 1 to 9]). PROCEDURES: All horses underwent at least 2 of the 3 procedures (EHC [n = 15], insulin-modified FSIGTT [18], and OGTT [18]) within a 10-day time frame to evaluate insulin sensitivity. RESULTS: Insulin sensitivity variables derived from the EHC and FSIGTT were strongly correlated (r = 0.88). When standardized to the same units of measure, these measures were still strongly correlated (r = 0.86) but were not equivalent. Area under the curve, peak insulin concentration, insulin concentration at 120 minutes, and 2 calculated indices from glucose and insulin data from the OGTT were significantly correlated with the EHC- and FSIGTT-derived estimates of insulin sensitivity. CONCLUSIONS AND CLINICAL RELEVANCE: In healthy Standardbreds with moderate body condition score, insulin sensitivities from the EHC and FSIGTT were strongly correlated but not equivalent. Estimates derived from an OGTT also may be useful to estimate insulin sensitivity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".