The global burden of illness of peanut allergy: A comprehensive literature review
Bibliographic record
Abstract
Peanut allergy (PA) currently affects approximately 2% of the general population of Western nations and may be increasing in prevalence. Patients with PA and their families/caregivers bear a considerable burden of self-management to avoid accidental peanut exposure and to administer emergency medication (adrenaline) if needed. Compared with other food allergies, PA is associated with higher rates of accidental exposure, severe reactions and potentially fatal anaphylaxis. Approximately 7%-14% of patients with PA experience accidental peanut exposure annually, and one-third to one-half may experience anaphylaxis, although fatalities are rare. These risks impose considerably high healthcare utilization and economic costs for patients with PA and restrictions on daily activities. Measures to accommodate patients with PA are often inadequate, with inconsistent standards for food labelling and inadequate safety policies in public establishments such as restaurants and schools. Children with PA are often bullied, resulting in sadness, humiliation and anxiety. These factors cumulatively contribute to significantly reduced health-related quality of life for patients with PA and families/caregivers. Such factors also provide essential context for risk/benefit assessments of new PA therapies. This narrative review comprehensively assessed the various factors comprising the burden of PA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".