An observational study on the management of digital dermatitis through a repeated risk assessment on 19 Dutch dairy herds
Bibliographic record
Abstract
The etiopathogenesis of bovine digital dermatitis (DD) is not well understood, but its risk factors on dairy farms have been studied extensively. The objective of this study was to identify associations between a DD risk score [determined by a DD risk assessment questionnaire (RAQ)] and DD prevalence (determined by an in-parlor M-score). We also investigated whether feedback for farmers on their DD management using the DD RAQ resulted in changes that decreased DD prevalence in their herds. The DD RAQ consisted of multiple-choice questions related to foot health, housing, and general management that were used to create a total risk score (TRS). In 2016 and 2018, the DD RAQ-together with a DD prevalence determination in the lactating herd-was used on 19 Dutch dairy farms from 1 veterinary practice. After each visit, farmers and their consulting veterinarians received a 1-page summary that identified herd-specific strengths and weaknesses in DD management. In 2018, the summary included suggestions for improvement. In 2019, farmers and veterinarians were contacted to ask whether the use of the DD RAQ and the 1-page summary had led them to implement changes in their DD management in 2016 and 2018. We tested the association between TRS and DD prevalence using linear mixed model analysis. The TRS ranged from 13 to 65% and 20 to 68% in 2016 and 2018, respectively. Herd DD prevalence ranged from 15 to 59% and 27 to 69% in 2016 and 2018, respectively. For both years, the DD RAQ identified that DIM, herd size, and breed were often present in a manner associated with increased risk for DD. The linear mixed model analysis identified that each 10-point increase in TRS was associated with an increase in herd DD prevalence of less than 1%. The association between TRS and herd DD prevalence was caused mainly by risk factors related to housing. We found no important relationship between change in TRS and change in DD prevalence between the 2 visits. Only a few farmers indicated some form of change in their DD management following a visit. Veterinarians in general said that they discussed the 1-page summaries and DD control with farmers during a routine visit, but the majority admitted a lack of follow-up. We propose that the DD RAQ could be used as a tool to start a discussion on DD control on farm, but simply undertaking a DD RAQ and providing a 1-page summary of the results was insufficient to initiate behavioral change that led to a decrease in DD prevalence.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".