Assessing the utility of leukocyte differential cell counts for predicting morbidity, mortality, and growth in a grain-fed veal facility: A prospective single cohort study
Bibliographic record
Abstract
Selective antimicrobial treatment strategies present a means to reduce antimicrobial use at the time of arrival at a veal or dairy beef operation. On-farm machine leukocyte differential cell counts (DCC) that can be acquired quickly may be useful to augment calf risk identification protocols. The objective of this study was to assess the utility of DCC taken at the time of arrival at a grain-fed veal facility and 72 h postarrival for determining morbidity risk, mortality risk, and growth during the production cycle. Data were collected between June and October 2018 from 240 calves upon arrival and from a subset of 160 calves 72 h postarrival at a commercial grain-fed veal facility in Ontario, Canada. Blood samples were evaluated using the QScout BLD test for leukocyte DCC (Advanced Animal Diagnostic, Morrisville, NC). All calves were screened using a standardized health examination, and a blood sample was collected to evaluate serum total protein and DCC. Cox proportional hazards models were constructed for both morbidity and mortality outcomes. Mixed linear regression models were constructed to evaluate average daily gain. Results from data collected at the time of arrival suggest that total protein values ≥5.2 g/dL reduced the hazard of mortality and that a rectal temperature >39.6°C was associated with an increased hazard of morbidity. Calves that were dehydrated gained less, whereas calves with an increased lymphocyte count had a higher rate of growth. Results from DCC collected 72 h postarrival suggest that lymphocyte counts between 4.8 and 5.8 × 10 9 cells/L decreased the hazard of mortality and counts >7.0 × 10 9 cells/L decreased the hazard of morbidity, whereas neutrophil counts >6.0 × 10 9 cells/L increased the hazard of mortality. This study demonstrates that machine DCC at the time of arrival and 72 h after arrival has potential for use in identifying high-risk calves that might require treatment, as part of selective antimicrobial therapy protocols, with the purpose of reducing antimicrobial use without sacrificing animal health in veal facilities.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".