Recent advances in the diagnosis and management of tree nut and seed allergy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Tree nut (TN) and seed allergies are frequent, and their prevalence appears to be on the rise. Allergic reactions associated with these foods are more frequently severe, and these allergies tend to persist into adulthood, consequently affecting quality of life. In this review, we summarize recent advances in diagnostic modalities and management strategies for TN/seed-allergic patients. RECENT FINDINGS: Clinical manifestations of TN and seed allergy range from asymptomatic sensitization to severe anaphylactic reactions. The use of emerging diagnostic tools such as component resolved diagnostics (CRD) and the basophil activation test (BAT) can help better predict clinical reactivity, the latter being currently reserved for research settings. Strict avoidance of all TN is generally not required, as most patients can tolerate select TN despite co-sensitization. Oral immunotherapy (OIT) is a promising alternative treatment instead of complete avoidance of culprit allergens, as it can safely increase the allergy threshold. SUMMARY: Our recent understanding of co-reactivity between various TN and seeds has shaped management opportunities, including select TN introduction and optimization of OIT, two strategies which may improve quality of life. There is a need for better minimally invasive diagnostic methods for TN and seed allergy, with CRD and BAT being promising tools.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".