Comparison of Two Different Protocols for the Treatment of Acute Escherichia coli Mastitis in Dairy Cattle
Bibliographic record
Abstract
E-coli mastitis is one of the most frequent causes of environmental mastitis in the dairy cattle worldwide. The purpose of this field study was to compare the efficacy of ceftiofur (HCL) in conjunction with supportive measures versus supportive measures alone for treatment of dairy cows affected with naturally occurring acute form of E. coli mastitis. From January 2014 to December 2016 a total number of 100 cows naturally affected by acute E-coli mastitis randomly were allocated into two groups. A milk sample from the affected quarter was collected for bacteriological tests on the first day of treatment. In group A (control), fifty cows received ceftiofur (HCL) 1mg/5kg/BW, flunixin meglumine 2.2mg/kg, calcium borogluconate 40%, 250ml and hypertonic saline (Nacl 7.2 %,) 5ml/kg. In group B (treatment, n=50), cows received the same drugs mentioned for group A, except ceftiofur (HCL) which replaced by placebo. In the group A, 41cows (82%) and in the group B, 2 cows (4%) were survived respectively. The rates of quarter health recovery in the groups A and B were 31.7% and 0% respectively. The differences between two groups were significant (P≤0.01). In conclusion our results indicated that treatment of cows affected with naturally occurring acute form of E. coli mastitis without application of effective antibiotic(s) such as ceftiofur (HCL) and fluid therapy almost impossible.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".