New Insights on the Relation Between Human Microbiota and Food Allergy
Bibliographic record
Abstract
Food allergy onset occurs in early life, in fact an Australian non-selected 1-year-old children cohort showed a prevalence of sesame, peanut and egg allergy of 8.9, 3.0 and 0.8 % respectively.Other experiments showed that clinical reactivity to such foods arises around 4 months of age.Several researchers reported dysbiosis in food allergic subjects.Some studies about human microbiome genetics evidenced altered gut microbiota in individuals suffering from both cow's milk and egg allergies.It is generally accepted that dysbiosis arises prodromal to the food allergy onset.In fact, after analyzing gut microbiota in children, belonging to a Canadian cohort, within 12 months of age, it was found proof of food sensitization after 12 months of age.The aim of this opinion paper is to enforce existing evidences and, above all, to introduce the concept that, in many cases, human features and perceptions are mostly shaped by microbiota, as outlined in my previous papers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".