Investigation of quarter-selective dry cow therapy based on bacteriological outcomes on dairy farms
Bibliographic record
Abstract
To achieve more targeted antibiotic use, this research paper addresses the investigation of a quarter-selective dry cow therapy (QSDCT) on 16 commercial dairy farms based solely on the pathogen species detected. Cytobacteriological analysis was performed on quarter milk samples collected 2 weeks prior to drying off and 3 to 5 days after calving. Treatment decisions were based on results before dry-off: Only quarters infected with major bacterial pathogens were treated with antibiotics. To prevent new intramammary infections (IMI), all quarters received an internal teat sealant. A total of 1,155 dry periods were evaluated. Only 8.1% of all quarters (range per farm 2.6% - 28.8%) were treated with antibiotics at dry-off and a high bacteriological cure risk of 97.1% was determined for IMI in these antibiotic-treated quarters. For IMI caused by minor pathogens a self-cure risk of 82.1% was observed. The risk of new IMI after calving was 14.6%. Results of binomial logistic regression models indicated that self-cure of IMI by minor pathogens was not related to the pathogen group, the level of quarter somatic cell count at dry-off, or the presence of at least one other quarter infected with minor pathogens in a cow. Furthermore, the risk for new IMI in uninfected quarters was not increased by the presence of at least one quarter infected with major pathogens within cow. However, 95.4% of all IMI by major pathogens after calving were due to new IMI. In conclusion, a pathogen-based QSDCT can be successfully applied on commercial dairy farms to reduce the antibiotic use, but more attention should be paid to prevent new IMI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".