Missing pieces of the puzzle to effectively control digital dermatitis
Bibliographic record
Abstract
Since the first report of bovine digital dermatitis (DD) in 1974, there is a large body of the literature published; however, effective prevention and control of the disease remain elusive. Although many aspects of the pathogenesis of DD have been investigated, even some of the most basic questions such as the aetiology of this disease remain under debate. Treponema spp. have been strongly associated with DD lesions and occur in abundance in advanced lesions; however, efforts to induce disease with pure cultures of these organisms have been largely underwhelming and inconsistent. Furthermore, although the disease has been presented for several decades, there is limited scientific evidence regarding effective treatment of DD. Apparent discrepancies between effectiveness in vitro and in vivo have challenged the scientific community to identify new potential treatment options. With no treatment resulting in a 100% cure rate, the current expectation is manageable control, but prospects for the eradication of the disease are unlikely using current approaches. In order to develop more effective approaches to control DD on-farm, there is a critical need for a deeper understanding regarding the causation, ecology, transmission and treatment of this disease. In this article, we attempt to provide insights into specific research needs related to DD in order to assist the industry, researchers, pharmaceutical companies and research sponsors with decision-making and identified research gaps.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".