The effect of tube versus bottle feeding colostrum on immunoglobulin G absorption, abomasal emptying, and plasma hormone concentrations in newborn calves
Bibliographic record
Abstract
The objective of this study was to determine if feeding colostrum to newborn calves through an esophageal tube, compared with a nipple bottle, would delay abomasal emptying, which would in turn decrease passive transfer of IgG and plasma glucose, insulin, and glucagon-like peptide (GLP) 1 and GLP-2 concentrations. Twenty newborn Holstein bull calves were fed 3 L of colostrum replacer (200 g of IgG) through either an esophageal tube or nipple bottle at 2 h after birth followed by feeding pooled whole milk every 12 h after birth. Acetaminophen was mixed into the colostrum meal as a marker for abomasal emptying. A jugular catheter was inserted 1 h after birth and blood was sampled frequently to analyze serum for IgG and acetaminophen and plasma for glucose, insulin, GLP-1, and GLP-2. Feeding method did not affect abomasal emptying, and as a result no treatment effect was present on serum IgG concentrations. Maximum concentration of serum IgG was 24.4 ± 0.40 mg/mL (± standard error), which was reached at 14.6 ± 1.88 h after the colostrum meal for both groups. Apparent efficiency of absorption at maximum concentration of IgG was 52.9%, indicating high efficiency of passive transfer of IgG for both treatments. Tube feeding increased glucose and insulin area under the curve before the first milk meal, most likely due to the decreased time to consume the colostrum meal. In addition, tube-fed calves consumed 0.5 ± 0.13 L more milk in their first milk meal than bottle-fed calves. No treatment effect on plasma concentrations of GLP-1 or GLP-2 was present, but both hormones increased after colostrum feeding. These findings confirm that there is no effect on absorption of IgG from colostrum when feeding good-quality colostrum at a volume of 3 L through either an esophageal tube or nipple bottle.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".