A randomized controlled trial of dexamethasone as an adjunctive therapy to propylene glycol for treatment of hyperketonemia in postpartum dairy cattle
Bibliographic record
Abstract
Treatment of hyperketonemia with oral propylene glycol has proven efficacy but the cure rate remains moderate. Dexamethasone has long been suggested as a treatment for hyperketonemia, even though evidence of its efficacy is contradictory. The objective of this randomized controlled trial was to evaluate the effect of adding a single intramuscular injection of 20mg of dexamethasone to oral propylene glycol therapy for hyperketonemia [blood β-hydroxybutyrate (BHB) ≥1.2mmol/L]. All cows between 3 and 16d in milk on 4 dairy farms in New York State were tested once weekly for hyperketonemia using a handheld ketone meter. All enrolled animals received 312g (300mL) of propylene glycol orally once daily for 4d and either a single injection of dexamethasone or an equivalent volume of sterile saline. A total of 509 animals were enrolled, with 254 and 255 in the placebo and dexamethasone groups, respectively. Treatment with dexamethasone decreased the odds of being hyperketonemic in the second week posttreatment; however, the odds of hyperketonemia in the first week posttreatment only decreased in those animals that were treated at a BHB blood concentration between 1.2 and 1.5mmol/L. For the 8% of cows with blood BHB >3.2mmol/L at enrollment, receiving dexamethasone increased the odds of being hyperketonemic the following week. We detected no difference between treatment groups in the odds of postpartum disease or in milk production. For cows with initial BHB of 1.2 to 1.5mmol/L, treatment with dexamethasone tended to reduce the odds of pregnancy at first insemination. Based on the small and conditional benefits of dexamethasone and a lack of difference in milk yield or disease incidence, we do not recommend the use of dexamethasone to treat hyperketonemia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".