The Effect of Prepartum Injection of Vitamin E on Health in Transition Dairy Cows
Bibliographic record
Abstract
The objective of this study was to investigate parenteral vitamin E for the prevention of peripartum disease in dairy cows. A randomized clinical trial was conducted in 21 commercial dairy herds. Cows (n = 1142) were randomly assigned to receive either a single subcutaneous injection of 3000 IU of vitamin E, or placebo, 1 wk before expected calving. Serum alpha-tocopherol was significantly increased in treated cows at 7 and 14 d, but not at 21 d after injection. Overall, there were no significant differences between treatment groups in the incidence of retained placenta, clinical mastitis, metritis, endometritis, ketosis, displaced abomasum, or lameness. However, there was a conditional benefit of treatment for reduction of the incidence of retained placenta. Cows with marginal pretreatment vitamin E status (serum alpha-tocopherol to cholesterol mass ratio < 2.5 x 10(-3)) that received an injection of vitamin E tended to have reduced risk of retained placenta. However, in cows with adequate serum vitamin E, there was no reduction in the incidence of any disease. For clinical application, primiparous animals were most likely to benefit from prepartum injection of vitamin E.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".