Prevalence of hock, knee, and neck skin lesions and associated risk factors in dairy herds in the Maritime Provinces of Canada
Bibliographic record
Abstract
Skin lesions are commonly seen in dairy herds and have been associated with animal-, environmental-, and management factors. These lesions are not only a welfare concern, but they also affect profitability. Three areas on the cattle were examined for skin lesions: the hock, knee, and neck. Previous Canadian studies estimating the prevalence of lesions and the risk factors associated with them have not included the Maritime Provinces. In this study, 73 herds in the Maritime Provinces were chosen voluntarily to participate, with both tiestalls (n = 33) and freestalls (n = 40) represented. Within each herd, 67 to 90% of the lactating cows were selected and assessed for potential animal-, environmental-, and management-based risk factors. If producers were aware of the potential risk factors, this could help them reduce the prevalence in their herd. Leg lesions were scored on a 4-point scale (0-3) based on hair loss, swelling, and scabs, with a lesion defined as a score of 2 or 3 on at least 1 hock or knee. Necks were scored on a 3-point scale (0-2), with a lesion defined as score 2. For freestalls, the prevalence (95% confidence interval) of hock lesions was 39% (29-49%), knee lesions was 14% (11-18%), and neck lesions was 1% (<1-2%). Similarly, for tiestalls the prevalence (95% confidence interval) of hock lesions was 39% (33-46%), knee lesions was 17% (13-22%), and neck lesions was 5% (3-8%). Due to differences in management and methods of assessment between facility types, tiestalls and freestalls were analyzed separately. Due to dichotomization of cows as having a skin lesion or not, random-effects multivariable logistic regression was used to determine the risk factors for each lesion and facility type. Several environmental-based measurements, such as the stall base, type and dryness of bedding, and type of milking parlor, were associated with leg lesions. An environmental-based measurement that was associated with neck lesions was the design of the feed rail barrier in freestalls and the dimensions of the tie rail in tiestalls. Animal-based risk factors, such as stage of lactation, parity, and body condition, were also associated with all 3 types of lesions. This study showed that lesions to the hock, knee, and neck were common in the Maritime Provinces of Canada. Although differences were seen between facility types, in general, the results suggest that improving stall design and management and feed bunk design would help producers reduce the number of skin lesions seen in dairy cattle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".