Randomized clinical trial of ketoprofen or ceftiofur for treatment of metritis in dairy cows
Bibliographic record
Abstract
Our objectives were to compare the efficacy of ketoprofen or ceftiofur for treatment of metritis in dairy cows considering subsequent health, production, and reproduction. Cows from 2 commercial dairy farms in Ontario, Canada were examined with a Metricheck device 3 times per week from 2 to 14 DIM. Cows with metritis (fetid vaginal discharge; n = 193) were blocked by parity and fever (rectal temperature ≥39.5°C or <39.5°C) and within each block per farm, randomly assigned to receive 3 mg/kg BW of ketoprofen (KET) or 2.2 mg/kg of ceftiofur hydrochloride (CEF), once a day for 3 d. Day of enrollment was considered study d 0. Rectal temperature and attitude were evaluated in cows with metritis on study d 0, 3, 4, 7, 10, and 13, and vaginal discharge was evaluated on study d 4, 7, 10, and 13. Body condition was scored at enrollment and at 35 DIM, and serum concentration of haptoglobin was measured at d 0, 2, 4, and 7. Cows with rectal temperature ≥39.5°C or a depressed attitude on d 3 were classified as clinical failure and received treatment with ceftiofur for 3 d (KET), or 2 additional days (CEF), to a maximum of 5 d of treatment with ceftiofur. At 35 ± 3 DIM cows were examined for uterine involution via transrectal palpation, purulent vaginal discharge (PVD) via Metricheck, and endometritis via endometrial cytology. Time to onset of cyclicity was assessed by serum progesterone (P4) measurements at 28, 42, and 56 DIM. Contemporary cows from the same farms without metritis (NOMET; n = 1,043) were used for comparison. Data were analyzed with mixed linear or logistic regression or Cox's proportional hazard models, including herd as a random effect. The proportion of clinical resolution of metritis on d 3 (96% vs. 92%), of cows with fever (from d 3 to d 13 after enrollment) or fetid discharge (from d 4 to d 13 after enrollment), and the number of medical treatments (3.1 vs. 3.3) were not different between CEF and KET, respectively. Cows in KET received fewer antibiotic treatments than cows in CEF (0.3 vs. 3.1). Uterine involution, prevalence of PVD (50% vs. 47%) and subclinical endometritis (6.6% vs. 4.3%), and proportion of cyclic cows (82% vs. 86%) did not differ between CEF and KET. Cows in KET had greater serum haptoglobin concentration from d 2 to 7 after enrollment. The incidence of mastitis, lameness, or displaced abomasum to 60 DIM and subclinical ketosis to 21 DIM did not differ among CEF, KET, and NOMET. There were no differences in median time to first AI (CEF = 68 d, 95% CI: 65-70; KET = 69 d, 95% CI: 68-72; NOMET = 69 d, 95% CI: 68-70), median time to pregnancy (CEF = 118 d, 95% CI: 92-145; KET = 113 d, 95% CI: 90-135; NOMET = 105 d, 95% CI: 101-109), pregnancy at first AI at 33 d after insemination (CEF = 42%; KET = 41%; NOMET = 41%), pregnancy loss after first AI (CEF = 8%; KET = 11%; NOMET = 8%), hazard of pregnancy, or hazard of culling up to 300 DIM. Milk yield was not different between CEF and KET during the first 10 wk, but was lesser in KET at wk 2 and 4 and CEF at wk 2, 4, and 6 than in NOMET. In this pilot-scale study, given early detection, we did not detect differences in subsequent health, milk yield, or reproductive performance in cows with metritis initially treated for 3 d with CEF or KET. Additional, larger studies are warranted.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".