Antibiotic dry cow therapy, somatic cell count, and milk production: Retrospective analysis of the associations in dairy herd recording data using multilevel growth models
Bibliographic record
Abstract
Antibiotic dry cow therapy (DCT) is an important part of most mastitis control programs. Updating DCT recommendations is an ongoing topic due to the global problem of antimicrobial resistance. Finland, along with other Nordic countries, has implemented selective DCT for decades. Our study analyzed Dairy Herd Improvement (DHI) information from 241 Finnish farmers who participated in a survey about their drying-off practices. The aim was to evaluate herd-level associations between milk somatic cell count (SCC), milk production, and various antimicrobial DCT approaches both cross-sectionally in 2016 and longitudinally in 2012-2016. The three DCT approaches in the study were selective, blanket, and no DCT use. An additional aim was to evaluate whether dynamic changes occurred in herd-average SCC and annual milk production over five years, and whether these potential changes differed between different DCT approaches. The method for the longitudinal analyses was growth modeling with random coefficient models. Differences in SCC and milk production between farms with different DCT approaches were minor. Regardless of the farm's DCT approach, annual milk production increased over the years, while average SCC was reasonably constant. The variability in SCC and milk production across all DCT groups was low between years, and most of the variability was between farms. Compared to other milking systems, farms with automatic milking system (AMS) had higher SCC, and in 2016 higher milk production. The results of this study suggest that it is possible to maintain low herd-average SCC and good milk production when using selective DCT and following the guidelines for prudent antimicrobial use. Average SCC and milk production varied across the herds, suggesting that advice on DCT practices should be herd-specific. The methodology of growth modeling using random coefficient models was applicable in analyzing longitudinal data, in which the time frame was relatively short and the number of herds was limited.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".