Validation of an automated cell counter to determine leukocyte differential counts in neonatal Holstein calves
Bibliographic record
Abstract
Recent advances in the understanding of risk factors and biomarkers in calves entering rearing facilities show promise for identifying high-risk calves on arrival at veal and dairy beef operations. Rapid automated leukocyte differential cell counts may be a good addition for augmenting or refining calf risk identification on-farm. The objective of this study was to validate an automated leukocyte cell counter, the QScout BLD test (Advanced Animal Diagnostics, Morrisville, NC), for its ability to determine leukocyte differential cell counts in neonatal Holstein calves. From June to July 2018, blood samples collected in EDTA anticoagulant from 235 calves upon arrival at an independent veal research facility in Ontario, Canada, were evaluated using the QScout BLD test and manually by microscopy. We compared these leukocyte differential counts using Lin's concordance correlation coefficient (ρ) and found very good agreement between tests for neutrophil counts (ρ = 0.83); fair agreement for lymphocyte counts (ρ = 0.32); fair agreement for the ratio of neutrophils to lymphocytes (ρ = 0.36); slight agreement for monocyte counts (ρ = 0.14); and slight agreement for eosinophil counts (ρ = 0.20). We further examined test results to determine if they differed in their classification of samples as being above, within, or below reported 95% reference intervals for neonatal Holstein calves. Classification between tests resulted in very good agreement for neutrophils and lymphocytes, with only 4.2% and 5.8% disagreement in classification, respectively. We observed moderate agreement for monocytes, with 23.3% classified differently, and poor agreement for eosinophils, with 70.3% classified differently. Further study is required to determine the role of leukocyte profiling in the risk assessment of calves arriving at calf-rearing 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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".