Preliminary validation of a calf-side test for diagnosis of failure of transfer of passive immunity in dairy calves
Bibliographic record
Abstract
The objective of this study was to evaluate the utility of an initial version of a calf-side test (ZAPvet Bovine IgG test, ZBx Corp., Toronto, ON, Canada) for diagnosis of failure of transfer of passive immunity (FTPI) in dairy calves. Blood samples (n=202) were collected from calves from 1 to 11d of age. Serum IgG concentration was determined by radial immunodiffusion (RID) assay. The mean IgG concentration was 1,764±1,035mg/dL, with a range from 133 to 5,995mg/dL. The ZAPvet Bovine IgG test was used to assess FTPI (serum IgG <1,000mg/dL) and test characteristics were calculated. The number of samples that had FTPI from the RID assay and ZAPvet test was 55 and 96 samples, resulting in a true prevalence of 27% and an apparent prevalence of 47.5%, respectively. The sensitivity, specificity, and positive and negative predictive values of the ZAPvet test were 0.82, 0.65, 0.47, and 0.91, respectively. The results of the ZAPvet test were derived from 2 observers, and the overall level of agreement between the results of the 2 observers was 84%, with a kappa value of 0.67. The ZAPvet Bovine IgG test showed good potential for further development as a cost-effective, rapid calf-side test for monitoring FTPI in dairy calves.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".