Predictors of diarrhea, mortality, and weight gain in male dairy calves
Bibliographic record
Abstract
The objective of this prospective cohort study was to determine the effect of an abnormal fecal consistency score on weight gain and mortality in male Holstein calves and to identify risk factors associated with the occurrence of an abnormal fecal consistency score. This study enrolled 2,616 calves entering a calf-raising facility in Ontario, Canada, between January 2018 and December 2020. Fecal consistency scores were assigned twice daily for the first 28 d following arrival, where a score of 2, indicating runny consistency, and 3, indicating watery consistency, were classified as diarrhea. Severe diarrhea was classified by a score of 3. Serum total protein was measured upon arrival and the source of the calf (i.e., whether the calf came from a drover, local farm, or auction) was recorded. Body weight measurements were also collected at arrival and at 14, 56, and 77 d after arrival. Calf mortality and disease treatment during the first 77 d were recorded. On average, calves had diarrhea for 16% (4.51 d) of the first 28 d under observation, and severe diarrhea for 7% (1.87 d) of the 28 d under observation. Using a repeated measures linear regression model, we found the proportion of days with diarrhea significantly decreased weight gain at 14, 56, and 77 d following arrival. An increased proportion of days with diarrhea increased the risk of mortality, which was determined using a Cox proportional hazards model. We also found, using 2 Cox proportional hazards models, that a higher proportion of days with an abnormal fecal score increased the hazard of antibiotic treatment. With respect to factors associated with the occurrence of abnormal fecal consistency, we found that arrival weight and the source of calves were statistically significant predictors. Specifically, for every additional kilogram of body weight at arrival, the proportion of days with diarrhea decreased by 7%. With respect to source, calves from drovers had a higher proportion of days with diarrhea compared with those sourced directly from local dairy farms. Our results highlight the substantial influence the presence of abnormal fecal consistency has on short-term weight gain, mortality risk, and morbidity risk. We also demonstrate that diarrhea occurrence can be predicted using body weight at arrival and calf source. Further research should evaluate longer-term effects of diarrhea and better understand the effect of arrival weight on neonatal calf disease risk.
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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 | 0.002 | 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 | 0.001 |
| 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".