The Use of Epinephrine in Acute Allergic Reaction to Food
Bibliographic record
Abstract
A 2-year-old boy presents in the emergency department (ED) for hives on his torso and arms.His medical history is unremarkable except for an egg allergy.His parents are certain he has not been in contact with egg.The pruritus and hives appeared spontaneously about 20 minutes prior.The last meal was more than 2 hours ago and contained no suspect ingredient.The patient is in no apparent distress and vital signs are within normal limits.His lungs are clear.Ear-nose-throat (ENT) exam reveals conjunctivitis and rhinitis.He is given oral diphenhydramine, but within 5 minutes, he reports throat pruritus and the mother notes a change in voice.The nurse asks whether she should administer the intramuscular (IM) epinephrine. CASE 2A father calls the on-call service concerning his teenage daughter who is allergic to peanuts.He is worried that she may have accidently eaten a cookie that contained peanuts.He sounds very anxious and is asking what to do and whether he should administer the epinephrine auto-injector.The daughter does not report any itching or skin symptoms.Her breathing appears to be normal with no wheezing and she does not report any gastro-intestinal symptoms.Upon realizing that she may have eaten something containing peanut, she began to panic, cry, and feel dizzy.The father is wondering if he should drive to the hospital rather than wait for the paramedics.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".