Use of Donkey Milk in Cases of Cow’s Milk Protein Allergies
Bibliographic record
Abstract
Human breast milk is the best nutritional support that insure the right development and influence immune status of the newborn infant. However, when it is not possible to breast feeding may be necessary to use commercial infant formulas that mimic, where possible, the levels and types of vitamins, minerals and other nutrients present in human milk. Despite this, some formula-fed infant develops allergy, atopic disease and differences in response to infection with respect to breast-fed infants. Donkey milk may be considered a good substitute for dairy cow’s milk derivatives in feeding children with severe Cow’s Milk Protein Allergy (CMPA) since its composition is closer to human milk compared to other species commonly bred. It has been proposed as an alternative to cow’s milk for children affected by CMPA when it is not possible breast feeding. Donkey milk is characterized by a low casein content, with values very close to human milk, and also total whey protein content in donkey milk is very close to that found in human milk but higher compared to bovine milk. Donkey milk has been used in several clinical trials involving children affected by CMPA because of the low allergenicity of this milk. The results shown in this review confirmed the nutritional characteristics of the protein fractions of donkey milk and the possibility of using donkey milk in feeding children with CMPA, particularly after an adequate lipid integration, including children with multiple food allergies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".