Peanut sensitization pattern in Norwegian children and adults with specific IgE to peanut show age related differences
Bibliographic record
Abstract
BACKGROUND: Peanuts contain potent food allergens and the prevalence of allergy is reported to increase, especially in children. Since peanut sensitization may differ between different geographical regions, we wanted to investigate the sensitization pattern to the individual peanut allergens in a Norwegian population. METHODS: Cases reported to the Norwegian Food Allergy Register with sera positive to peanut extract were analyzed for specific IgE (sIgE) to the recombinant peanut allergens Ara h 1, Ara h 2, Ara h 3, Ara h 8 and Ara h 9 and to birch pollen extract. Serum samples negative to the above allergens were analyzed for sIgE to Ara h 6, and sIgE to Pru p 3 in peach were analyzed in sera positive to the cross-reactive allergen Ara h 9. RESULTS: Highest frequency of sIgE to Ara h 2, often co-sensitized to Ara h 1 and 3, were found in the small children up to 6 years of age. From the age of 6 years, sensitization to Ara h 8 was predominant. The sIgE levels to the storage proteins Ara h 1, 2 and 3 were strongly correlated, as was the sIgE levels to Ara h 8 and birch pollen extract. A low sensitization rate of sIgE to Ara h 9 in young adults was observed, which sIgE levels were very strongly correlated to Pru p 3. CONCLUSION: The sensitization to peanut allergens in a Norwegian population shows a clear age dependent pattern. The results add to the previously published research on the sensitization patterns of peanut sensitized patients in different geographical areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".