Herd-level risk factors for hock injuries in freestall-housed dairy cows in the northeastern United States and California
Bibliographic record
Abstract
The aim of this study was to investigate the associations between management and facility design factors and the prevalence of hock injuries in high-producing dairy cows in 76 freestall herds in the northeastern United States (NE-US; Vermont, New York, Pennsylvania) and California (CA). One group of high-production multiparous cows was monitored on each farm, and data on management, facility and stall design, and the conditions of the hocks were collected. Focal cows [n=38 ± 3 (mean ± standard deviation)] were evaluated for hock injuries using a 3-point scale (where 1=healthy and 3=evidently swollen or severe injury). Measures associated with the proportion (logit-transformed) of cows having injuries (score ≥ 2) or severe injuries (score=3) at the univariable level were submitted to multivariable general linear models. In NE-US, overall hock injuries increased with the percentage of stalls with fecal contamination [odds ratio (OR)=1.26; 95% confidence interval (CI)=1.02-1.54, for a 10% increase], and with the use of sawdust as bedding (OR=3.47; CI=1.14-10.62), and decreased with deep bedding (i.e., at least 10 cm depth of any type of bedding; OR=0.05; CI=0.02-0.14), use of sand as bedding (OR=0.06; CI=0.02-0.15), bedding dry matter (DM) ≥ 83.9% (OR=0.08; CI=0.03-0.20), and access to pasture during the dry period (OR=0.17; CI=0.05-0.53). When these variables were submitted to a multivariable model, the presence of deep bedding was the only factor that remained significant, explaining 54% of the variation in overall injuries. Severe hock injuries increased with the use of automatic scrapers (OR=2.29; CI=1.11-4.71) and the percentage of stalls with fecal contamination (OR=1.14; CI=1.00-1.31, for a 10% increase), and decreased with sand bedding (OR=0.22; CI=0.10-0.49), deep bedding (OR=0.24; CI=0.11-0.52), bedding DM ≥ 83.9% (OR=0.28; CI=0.14-0.58), and access to pasture during the dry period (OR=0.42; CI=0.18-0.97). The final multivariable model, which explained 36% of the variation in severe hock injuries, included the use of automatic scrapers and deep bedding. In CA, stall stocking density (OR=1.41; CI=1.00-2.01, for a 10% increase) and poor bedding maintenance (OR=1.08; CI=1.01-1.16, for a 2.5-cm decrease in depth of deep-bedded stalls) were associated with an increase of overall and severe hock injuries, respectively. Deep-bedded and well-maintained stalls reduce the risk of hock injuries. Regional variation in risk factors for these injuries should be considered when formulating on-farm recommendations.
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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 | 0.001 |
| 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".