Epidemiology of Digital Dermatitis in Western Canadian Feedlot Cattle
Bibliographic record
Abstract
Digital dermatitis (DD) is an emerging disease in feedlot cattle. Our objective was to identify animal- and feedlot-level risk factors for DD by analyzing individual animal health records (n = 1,209,883) and feedlot-level records from western Canadian feedlots (n = 28) between 2014 and 2018, inclusive. The risk of a DD diagnosis was higher (incidence rate ratio (IRR) = 2.08, 95% CI 1.52 to 2.86) in cattle sourced from confined background operations (CB) versus cattle sourced from auction markets (AM). Conversely, ranch direct (RD) cattle were (IRR = 0.02, 95% CI 0.04 to 0.30) lower risk than AM cattle of being diagnosed with DD. The risk of being diagnosed with DD was higher in females than in males. The magnitude of the risk in females over males was influenced by annual DD incidence in low morbidity years (2014, 2017, and 2018) (IRR = 2.02, 95% CI 1.27 to 3.19), medium morbidity years (2016) (IRR = 2.95, 95% CI 1.64 to 5.33), and high morbidity years (2015) (IRR = 5.41, 95% CI 3.27 to 8.95). At the feedlot-level, the risk of a diagnosis of DD was lower in small capacity (SCF) versus large capacity feedlots (LCF) (IRR = 0.24, 95% CI 0.05 to 0.76). Future research should focus on identifying factors that may propagate disease transmission between cattle of different sexes and from different acquisition sources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".