Bibliographic record
Abstract
Introduction: Idiopathic anaphylaxis (IA) is a diagnosis of exclusion and is based on the inability to identify a causal relationship between a trigger and an anaphylactic event, despite a detailed patient history and careful diagnostic assessment. The prevalence of IA among the subset of people who experienced anaphylaxis is challenging to estimate and varies widely, from 10 to 60%; most commonly noted is ∼20% in the adult anaphylactic population. Comorbid atopic conditions, such as food allergy, allergic rhinitis, and asthma, are present in up to 48% of patients with IA. Improved diagnostic technologies and an increased understanding of conditions that manifest with symptoms associated with anaphylaxis have improved the ability to determine a more accurate diagnosis for patients who may have been initially diagnosed with IA. Methods: Literature search was conducted on PubMed, Google Scholar and Embase. Results: Galactose-α-1,3-galactose (α-gal) allergy, mast cell disorders, and hereditary a-tryptasemia are a few differential diagnoses that should be considered in patients with IA. Unlike food allergy, when anaphylaxis occurs within minutes to 2 hours after allergen consumption, α-gal allergy is a 3‐6-hour delayed immunoglobulin E‐mediated anaphylactic reaction to a carbohydrate epitope found in red meat (e.g., beef, lamb, pork). The more recently described hereditary α-tryptasemia is an inherited autosomal dominant genetic trait caused by increased germline copies of tryptase human gene alpha-beta 1 (TPSAB1), which encodes α tryptase and is associated with elevated baseline serum tryptase. Acute management of IA consists of carrying an epinephrine autoinjector to be administered immediately at the first signs of anaphylaxis. Long-term management for IA with antihistamines and other agents aims to potentially reduce the frequency and severity of the anaphylactic reactions, although the evidence is limited. Biologics are potentially steroid-sparing for patients with IA; however, more research on IA therapies is needed. Conclusion: The lack of diagnostic criteria, finite treatment options, and intricacies of making a differential diagnosis make IA challenging for patients and clinicians to manage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".