White paper peanut allergy
Bibliographic record
Abstract
Abstract The current management of a primary IgE-mediated peanut allergy consists of the two basic pillars “exposure prophylaxis” with avoidance of the allergen and “emergency therapy” with short-term treatment of an acute allergic reaction after accidental ingestion. Accidental reactions are common despite attempted avoidance. The severity of an allergic or even anaphylactic reaction after accidental ingestion is difficult to assess prior to reaction. In addition, reaction thresholds may vary depending on the accompanying augmentation factor. Therefore, every peanut allergic patient should receive individual dietary counseling as well as instructions for the use of the emergency kit and a structured patient education program (anaphylaxis group training), if necessary. For the first time, since fall 2021 a causal treatment option with a drug for oral immunotherapy will now be available for 4‑ to 17-year-old peanut-allergic children and adolescents. The oral immunotherapy with peanut protein as defatted powder of Arachis hypogaea L., semen (peanuts) leads to desensitization with a good efficacy record and an acceptable safety profile. Other treatment options with different therapeutic approaches are also under development and will probably expand the range for treatment in the coming years.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.149 | 0.052 |
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".