Successful implementation of a risk assessment and mitigation program to control bovine digital dermatitis at the herd-level
Bibliographic record
Abstract
This nonrandomized clinical intervention study was designed as a prospective, multicenter group comparison to evaluate the efficacy of a risk assessment and mitigation program to control bovine digital dermatitis (BDD). The program was implemented over a 1-year period on 9 intervention (INT) farms and 10 control (CTR) farms. Mainstays of the program derived from results of a previous BDD risk factor analysis. All farms were visited monthly to assess within-herd BDD prevalences to perform risk assessments and to treat BDD lesions with salicylic acid paste. Bulk milk samples were collected every 4 months. Diagnosis of BDD was based on visual inspection (clinical scoring) of the feet. Risk-associated management practices were identified on each farm, and management changes expected to prevent further introduction or spread of BDD within INT farms were suggested and agreed upon with farmers of the INT farms. Lesional biopsies were taken from a subset of cows of the INT group before and 2 months after treatment for histopathological and molecular biological examination to confirm histological and bacteriological cure in addition to clinical cure. The initial BDD prevalences for the INT and CTR farms averaged 39.8% (IQR 16.2) and 41.0% (IQR 12.4) for overall BDD lesions, 25.9% (IQR 10.8) and 26.2% (IQR 14.5) for active BDD lesions, and 22.1% (IQR 6.9) and 23.7% (IQR 22.3) for chronic BDD lesions, respectively. After 1 year of implementation, overall BDD prevalences were reduced to 14.1% (IQR 8.2) on INT farms but remained at 41.6% (IQR 10.8) on CTR farms. A significant decline in bulk milk anti-Treponema antibodies over the 1-year period was found in INT as compared to CTR farms. Considering the results of the histopathological examination, of 16S metagenomic sequencing and of the Fluorescence in situ hybridization as indicators for healing, 6/7 (85.7%) selected lesions were cured 2 months post completion of treatment. The results of this study show that the described BDD control measures can markedly reduce the within-herd prevalence of BDD. The proposed procedure might provide the basis for a nationwide BDD mitigation program that could be of importance also beyond national borders.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".