Quantifying the impact of frequent diseases and syndromes on calf health using the opinions of producers and veterinarians: Toward dairy calf disability weights
Bibliographic record
Abstract
The first objective of this study was to quantify the impact and disability weight (DW) of frequent diseases or syndromes of preweaning dairy calves using the perceptions of producers and veterinarians. The second objective was to compare the opinions of producers and veterinarians regarding the impact and DW of dairy calves' frequent diseases and syndromes. A survey was conducted to obtain demographic information and opinions of 39 dairy producers and 52 veterinarians on the impact of frequent disease and syndromes on calf health. Most of the producers (97.4%, 38/39) were clients of the ambulatory clinic at the Faculté de Médecine Vétérinaire de l'Université de Montréal in Saint-Hyacinthe, Québec, Canada. They were actively engaged in calf research projects. Québec veterinarians were contacted via email through their association. Additionally, veterinarians from the bovine ambulatory clinic and the bovine veterinary hospital at the Faculté de Médecine Vétérinaire de l'Université de Montréal were contacted directly via email. A visual analog scale, represented by a horizontal line ranging from 0 (no impact) to 10 (maximum impact; i.e., death or euthanasia), was used to estimate the impact of 9 frequent diseases or syndromes (diarrhea, dystocia, inadequate transfer of passive immunity, fracture, wound or abscess, arthritis, respiratory disease, umbilical infection, and congenital defect) on calf health following previously reported methods (using the most probable, and range of the perceived impact for each participant and disease). The DW values were obtained by converting the impact values to a probability density in a scale from 0 to 1 using BetaPERT methodology, a type of data distribution model. Average impact and DW were quantified for each frequent disease and syndrome. Average impact differed statistically across different diseases and syndromes. The highest average impacts were obtained for the presence of a fracture (6.49/10), arthritis (6.22/10), and congenital defects (6.03/10), whereas the lowest impact was observed for the presence of a wound or abscess (3.42/10). The opinions of producers and veterinarians were similar for most of the selected diseases and syndromes; however, statistical differences were observed for arthritis (producers = 5.13 vs. veterinarians = 6.88), umbilical infection (producers = 3.65 vs. veterinarians = 4.74), and dystocia (producers = 3.87 vs. veterinarians = 4.58). A strong correlation coefficient (0.72) was observed between the observed ranks of diseases and syndromes of producers and veterinarians. In conclusion, we quantified how frequent diseases and syndromes affect calf health. Producers and veterinarians mostly agreed on their impact. Estimating DW is a crucial first step in creating a health measure for dairy calves. Similar to humans, this metric will be important for health comparative analysis for producers, veterinarians, and industry.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".