Impact of digital dermatitis on locomotion and gait traits of beef cattle
Bibliographic record
Abstract
Digital dermatitis (DD) is an infectious skin disease and a major cause of lameness that significantly impacts cattle productivity and welfare. However, DD does not always result in lameness and lameness scoring systems are not specific to hoof pathologies. Digital dermatitis detection protocols could be improved by including gait traits most related to DD. The aims of this study were to 1) determine the association between DD M-stage ("M" for Mortellaro), locomotion, and gait traits: arched back (AB), asymmetric gait (AG), head bobbing (HB), tracking up (TU), and reluctance to bear weight (WB), and 2) determine which traits are most associated with DD. Cattle (n = 480) from three feedlots were enrolled. Locomotion score (LS) and gait traits were assessed as cattle walked four strides along a dirt alleyway. Next, cattle were restrained in a chute, each hind foot lifted, and DD M-stage (absent, active, or chronic) determined. The association between presence of DD, LS, and gait traits were scored independently (n = 291). For both LS and gait the lowest score represents normal and the highest score severely altered. Digital dermatitis presence was associated with higher LS (P < 0.001). Odds ratios (ORs) for cattle with DD being lame or moderately to severely lame were 8.0 (P < 0.001) and 10.1 (P < 0.001) times more than cattle without lesions. Cattle with active lesions had the greatest odds of being lame (OR = 9.4; P < 0.001). Digital dermatitis presence was associated with all gait traits (P < 0.001), where AG (OR = 5.5; P < 0.001) and WB (OR = 5.8; P < 0.001) had the greatest OR for classifying cattle with DD as having altered gait. The OR for cattle with active lesions having altered gait was greatest for WB which was 6.0 (P < 0.001) times greater than cattle without lesions. The OR for cattle with chronic lesions having altered gait was greatest for AG being 6.5 (P < 0.001) times more than cattle without lesions. All gait traits had low sensitivity (Se) for detecting cattle with DD and varied from 6.7% to 55.8%. Locomotion score (Se 55.8%) and AG (Se 44.2%) were most predictive with positive predictive values of 76.6% and 74.3%, respectively. Specificity for all traits ranged from 94.1% for LS to 98.4% for WB with negative predictive values of 72.1% and 68.9%, respectively. In conclusion, LS, WB, and AG had the strongest association with cattle that had DD. Locomotion scoring that includes a focus on WB and AG is the best tool to detect DD in beef cattle.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".