Comparing antibiotic treatment at dry-off on one quarter versus all quarters in cows with only one quarter affected with a high somatic cell count or an intramammary infection
Bibliographic record
Abstract
The aim of this randomized controlled study was to evaluate, on cows with only one quarter affected at dry-off with an IMI, a high SCC (>200,000 cells/mL), or both, the impact of treating with an antibiotic plus internal teat sealant only the quarter affected (treating unaffected quarters with teat sealant alone; SelectAB) versus all quarters (AllAB) on subsequent lactation SCC (analyzed as log 10 transformed SCC) and IMI (odds of cure [i.e., affected quarter at dry-off but not at calving]; and odds of healthy [i.e., not affected at dry-off and calving]). Ninety-six multiparous cows from 3 research herds were randomly assigned to SelectAB or AllAB. Linear and logistic regression mixed models were used for data analysis. Cows assigned to AllAB had a 0.15 units lower log 10 SCC (95% CI=−0.19 to −0.11 log points) through the full subsequent lactation compared with SelectAB cows. The odds of cure for an affected quarter were similar for AllAB and SelectAB cows (odds ratio=0.77; 95% CI=0.27–2.20), but cows under AllAB had higher odds of having a healthy quarter shortly after calving compared with SelectAB cows (odds ratio=2.86; 95% CI=1.21–6.73). In conclusion, a reduced rate of healthy quarters (i.e., increased new infections any time from dry-off to shortly after calving), especially in quarters treated with internal teat sealant alone within an udder with one quarter affected with IMI, high SCC, or both at dry-off, may be a major barrier for the implementation of a quarter-based dry-off treatment approach.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".