Evaluating the change in immunoglobulin G and accuracy of assessing transfer of passive immunity during the first 7 days of age in Holstein dairy calves fed colostrum replacer
Bibliographic record
Abstract
Accurate diagnosis of failure of transfer of passive immunity is an important component for dairy herd management goals and involves measurement of serum IgG in young calves. However, it is not well understood how IgG concentration changes over the first week of life. The primary objective of this cohort study was to evaluate how blood serum IgG concentrations change in dairy calves fed colostrum replacer during the first 7 d of life. This cohort study combined data collected from 4 different studies that evaluated different colostrum management strategies. Daily blood samples and health scores were collected during the first 7 d of life in male and female Holstein calves between May 2021 to August 2023 (n = 365). Serum was separated and analyzed in a commercial laboratory via radial immunodiffusion to determine IgG concentrations. Results were further categorized based on IgG concentration into "excellent" (25.0 g/L), "good" (18.0-24.9 g/L), "fair" (10.0-17.9 g/L), and "poor" (<10.0 g/L) categories. Mixed linear regression models were used to determine the effect of day of sampling relative to birth and d-1 transfer of passive immunity (TPI) classification on change in IgG concentration, whereas mixed ordinal logistic regression models were built to evaluate the odds of being in a different TPI category on d 2 through 7 compared with d 1 of life. A random effect for calf within trial was included in all models. The median (range) IgG concentration on d 1 (i.e., between 24 and 48 h of age) was 22.3 g/L (8.1-43.1 g/L) and decreased to a median of 11.7 g/L (4.8-60.1 g/L) on d 7. When IgG values were categorized, there was an increase in calves with poor TPI (3.3% of calves on d 1 to 33.5% of calves on d 7) across the first 7 d of life. In the mixed linear regression models, all days were statistically different from IgG measured on d 1. Specifically, IgG progressively decreased each day relative to d 1 until d 6. In the mixed effects ordinal logistic regression model, the odds of being categorized into a different passive immunity category on d 2 relative to d 1 based on IgG was 0.43 (95% CI = 0.29-0.63), which continued to decline on d 3 through 7. This study shows that calf age at the time of assessing TPI affects interpretation of serum IgG in calves fed colostrum replacer. Thus, serum IgG should be assessed between 24 and 48 h of age when feasible, to consistently evaluate passive immunity status when serum IgG is highest in colostrum replacer-fed 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".