Effectiveness of a standardized footbath protocol for prevention of digital dermatitis
Bibliographic record
Abstract
A footbath is the most common herd-level approach to control digital dermatitis (DD) in intensive dairy farming. However, wide variation in footbath design and protocols suggests a gap between evidence-based management practices and on-farm implementation. The objective was to evaluate effectiveness of a standardized footbath protocol in decreasing prevalence of active DD lesions in lactating dairy cattle. The protocol was based on current scientific literature, including footbath design and management. The M-stage classification system was used to score DD lesions. Scores were also combined into a simplified scoring system: no lesions (M0), active lesions (M1, M2, and M4.1), and chronic lesions (M3 and M4). A controlled intervention trial was conducted on 9 farms over 22 wk. Each farm served as its own control with data collected for 10 wk before and 12 wk after intervention. A total of 1,978 lactating cattle were assessed biweekly for DD lesions and leg cleanliness in the milking parlor. Lactating cattle were also inspected in the trimming chute at 3 time points: start, intervention, and end of trial. Intervention consisted of implementing an automated footbath that measured 3 m long, 0.50 m wide, held a fluid depth of 0.15 m, along with a weekly footbath protocol using 5% CuSO 4 for 4 consecutive milkings, with footbath content replaced at a maximum of 200 cow passes. Multilevel logistic regression models for repeated measures were used to evaluate effects of the standardized footbath protocol in preventing active DD lesions. For the purpose of analysis, farms' within-herd prevalence of active DD lesions (at baseline) was assessed and categorized as low (<15%) or high (≥15%). Farms with low or high within-herd prevalence of active DD lesions at trial outset had a mean cow-level prevalence of active DD lesions of 8% (range, 2 to 13) and 31% (range, 18 to 43), respectively. At milking parlor inspections, apparent prevalence of active DD lesions decreased from the time of footbath intervention, but this effect interacted with the farms' baseline prevalence of active DD lesions. In that regard, on farms with high prevalence of active DD at baseline, apparent prevalence of active DD lesions decreased after intervention, whereas on farms with low prevalence of active DD at baseline, apparent prevalence of active DD lesions did not change. At the cow level, poor leg cleanliness was associated with higher prevalence of active DD lesions. At trimming chute inspections, prevalence of active DD lesions decreased from start to the end of the trial (22 and 14%, respectively); concurrently, prevalence of feet with no DD lesions (M0) increased (39 and 48%). We concluded that on farms with high DD prevalence, implementation of proper footbath design and improvement of footbathing management will decrease prevalence of active DD lesions and increase prevalence of feet without DD lesions. In addition, improving cow cleanliness will further result in control of active DD lesions.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".