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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".