Management of nut allergy in primary care
Bibliographic record
Abstract
Nut allergy presents a growing public health burden and is the most common cause of fatal anaphylaxis in the UK.1 Adults and children commonly present to primary care with suspected reactions to nuts. The UK has a nut allergy prevalence of 0.7–2.5%.2 The British Society for Allergy and Clinical Immunology (BSACI) released their Guideline for the Diagnosis and Management of Peanut and Tree Nut Allergy 2 in July 2017 and the National Institute for Health and Care Excellence (NICE) Quality Standard Food Allerg y (QS118)3 reinforces expectations in food allergy management. Life-threatening primary nut allergy most often arises in early childhood, and is caused by the production of specific IgE (sIgE) antibodies, which recognise heat-resistant nut proteins. Signs of a reaction usually develop within a few minutes of ingestion, and include vomiting, oral tingling, itching, urticaria, and lip and eyelid angioedema. Symptoms such as airway swelling, hoarse voice, breathing impairment, and drowsiness are often not correctly recognised as indicating anaphylaxis. Not all patients who report oral symptoms, facial swelling, and urticaria shortly after eating nuts have life-threatening primary nut allergy. Patients with seasonal allergic rhinitis may develop cross-sensitisation between pollen allergens and structurally similar heat-labile proteins within nuts, which can present as pollen food syndrome (PFS). In these cases, the majority of IgE-mediated symptoms are oral-gastric because the labile nut proteins are broken down by gastric acid. Therefore, PFS rarely induces systemic symptoms and the prescription …
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".