Effects of dexamethasone on glucose dynamics and insulin sensitivity in healthy horses
Bibliographic record
Abstract
OBJECTIVE: To determine effects of dexamethasone on glucose dynamics and insulin sensitivity in healthy horses. ANIMALS: 6 adult Standardbreds. PROCEDURES: In a balanced crossover study, horses received dexamethasone (0.08 mg/ kg, IV, q 48 h) or an equivalent volume of saline (0.9% NaCl) solution (control treatment) during a 21-day period. Horses underwent a 3-hour frequently sampled IV glucose tolerance test (FSIGT) 2 days after treatment. Minimal model analysis of glucose and insulin data from FSIGTs were used to estimate insulin sensitivity (Si), glucose effectiveness (Sg), acute insulin response to glucose (AIRg), and disposition index. Proxies for Si (reciprocal of the inverse square of basal insulin concentration [RISQI]) and beta-cell responsiveness (modified insulin-to-glucose ratio [MIRG]) were calculated from basal plasma glucose and serum insulin concentrations. RESULTS: Mean serum insulin concentration was significantly higher in dexamethasone-treated horses than control horses on days 7, 14, and 21. Similarly, mean plasma glucose concentration was higher in dexamethasone-treated horses on days 7, 14, and 21; this value differed significantly on day 14 but not on days 7 or 21. Minimal model analysis of FSIGT data revealed a significant decrease in Si and a significant increase in AIRg after dexamethasone treatment, with no change in Sg or disposition index. Mean RISQI was significantly lower, whereas MIRG was higher, in dexamethasone-treated horses than control horses on days 7, 14, and 21. CONCLUSIONS AND CLINICAL RELEVANCE: The study revealed marked insulin resistance in healthy horses after 21 days of dexamethasone administration. Because insulin resistance has been associated with a predisposition to laminitis, a glucocorticoid-induced decrease in insulin sensitivity may increase risk for development of laminitis in some horses and ponies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".