Herd-level risk factors for lameness in freestall farms in the northeastern United States and California
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objective was to investigate the association between herd-level management and facility design factors and the prevalence of lameness in high-producing dairy cows in freestall herds in the northeastern United States (NE; Vermont, New York, Pennsylvania) and California (CA). Housing and management measures such as pen space, stall design, bedding type, and milking routine were collected for the high-producing pen in 40 farms in NE and 39 farms in CA. All cows in the pen were gait scored using a 1-to-5 scale and classified as clinically lame (score ≥3) or severely lame (score ≥4). Measures associated with the (logit-transformed) proportion of clinically or severely lame cows at the univariable level were submitted to multivariable general linear models. In NE, lameness increased on farms that used sawdust bedding [odds ratio (OR)=1.71; 95% confidence interval (CI)=1.06-2.76] and decreased with herd size (OR=0.94; CI=0.90-0.97, for a 100-cow increase), use of deep bedding (OR=0.48; CI=0.29-0.79), and access to pasture (OR=0.52; CI=0.32-0.85). The multivariable model included herd size, access to pasture, and provision of deep bedding, and explained 50% of the variation in clinical lameness. Severe lameness increased with the percentage of stalls with fecal contamination (OR=1.15; CI=1.06-1.25, for a 10% increase) and with use of sawdust bedding (OR=2.13; CI=1.31-3.47), and decreased with use of deep bedding (OR=0.31; CI=0.19-0.50), sand bedding (OR=0.32; CI=0.19-0.53), herd size (OR=0.93; CI=-0.89-0.97, for a 100-cow increase), and rearing replacement heifers on site (OR=0.57; CI=0.32-0.99). The multivariable model included deep bedding and herd size, and explained 59% of the variation of severe lameness. In CA, clinical lameness increased with the percentage of stalls containing fecal contamination (OR=1.15; CI=1.05-1.26, for a 10% increase), and decreased with herd size (OR=0.96; CI=0.94-0.99, for a 100-cow increase), presence of rubber in the alley to the milking parlor (OR=0.46; CI=0.28-0.76), distance of the neck rail from the rear curb (OR=0.97; CI=0.95-0.99, for a 1-cm increase), water space per cow (OR=0.92; CI=0.85-0.99, for a 1-cm increase), and increased frequency of footbaths per week (OR=0.90; CI=081-0.99, for a 1-unit increase). The multivariable model included herd size, percentage of stalls containing fecal contamination, and presence of rubber in the alley to the milking parlor, and explained 44% of the variation of clinical lameness. Severe lameness increased with the percentage of stalls containing fecal contamination (OR=1.23; CI=1.06-1.42, for a 10% increase) and decreased with frequency of manure removal in the pen per day (OR=0.72; CI=0.53-0.97, for a 1-unit increase). The final model included both variables and explained 28% of the variation in severe lameness. In conclusion, changes in housing and management may help decrease the prevalence of lameness on dairy farms, but key risk factors vary across regions.
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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 it