Risk factors of digital dermatitis in feedlot cattle
Bibliographic record
Abstract
Abstract Digital dermatitis (DD) has been reported in North American feedlots, although risk factors are not well characterized. Our objectives were to analyze: (1) foot and leg conformation and (2) pen hygiene, as potential variables that predispose feedlot cattle to DD. Production parameters in DD-affected cattle were compared with healthy cattle and with those diagnosed with more commonly known infectious lesion foot rot (FR). In total, 2,854 feedlot cattle in 11 pens in 2 feedlots were assessed (bi-weekly pen walks) throughout the feeding cycle. Pen condition was categorized as: “dry,” “mud present but has good bedding,” “more mud than bedding,” and “excessive mud.” Gait scoring was competed and cattle with abnormal gait or evident foot lesions (i.e., DD or FR) were restrained in a cattle chute for a close foot inspection (n=280), including scoring of foot angle and claw set and hind and side views of rear feet and legs. Cumulative incidence of DD (present or absent) and FR was 2.5% (71/2,854) and 11.6% (331/2,854), respectively. Foot and leg conformation was not significantly different between left and right sides or between cattle with (n=71) and without DD (n=209). Lameness was diagnosed in only 22% of cattle with DD. Cattle with DD gained 0.27 kg/d less compared with healthy cattle (mean ± SD: 1.29 ± 0.29 vs. 1.56 ± 0.27, P<0.05) and 0.4 kg/d less compared with FR (1.29 ± 0.29 vs. 1.69 ± 0.25). Presence of DD was not significantly different between pens with “dry” and “mud present but has good bedding,” but for pens with “more mud than bedding” or “excessive mud,” the risk of cattle having DD cases increased significantly [odds ratio (OR)=8.55, confidence interval (CI): 4.0–18.4 and OR=14.1, CI: 5.9–33.8, respectively]. In conclusion, it is important to keep good pen conditions to reduce the risk of DD, which can be managed through proper stocking density and strategic bedding, irrespective of foot and leg conformation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".