Effect of Dexamethasone on Resting Blood Lactate Concentrations in Horses
Bibliographic record
Abstract
BACKGROUND: Blood lactate concentration is a marker of tissue perfusion and helps guide therapeutic interventions in critically ill horses. In both humans and dogs, administration of corticosteroids can increase blood lactate concentration, leading to type B hyperlactatemia. This effect could be a consequence of the impact of corticosteroids on glucose metabolism. OBJECTIVES: To investigate the effects of daily IM dexamethasone administration on blood lactate and glucose concentrations in horses. ANIMALS: Nine healthy adult horses. METHODS: A randomized, blinded, controlled, cross-over study design was used. Horses were randomly assigned to 1 of 2 groups, either receiving 0.05 mg/kg of dexamethasone IM or an equivalent volume of saline, daily for 7 days. Blood was collected to determine lactate and glucose concentrations at baseline, 2 hours after the daily injections and 24 hours after the last injection. RESULTS: Dexamethasone treatment had a statistically significant effect on lactate (P = .006) and glucose (P = .033) concentrations. The least squares mean lactate concentration was 0.93 mmol/L (95% CI: 0.87-0.99) in the dexamethasone group compared to 0.71 mmol/L (95% CI: 0.70-0.73) for the saline group. A positive relationship between blood lactate and glucose concentrations was identified, with a 0.07 mmol/L (95% CI: 0.05-0.09) increase in lactate concentration per unit increase in glucose (P < .0001) concentration. CONCLUSIONS AND CLINICAL IMPORTANCE: Dexamethasone induces statistically significant increases in blood lactate and glucose concentrations in healthy horses. Awareness of the potential for corticosteroids to induce type B hyperlactatemia might be important in the management of critically ill horses receiving dexamethasone.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".