Treatment of initial allergic reactions to peanut inside and outside of health care facilities
Bibliographic record
Abstract
Recent studies suggest increased admission rates for food-related anaphylaxis. The only effective treatment for anaphylaxis is prompt administration of epinephrine. To characterize treatment practices of initial allergic reactions inside and outside health care facilities (HCF). Individuals with an allergist-confirmed peanut allergy were recruited from the Montreal’s Children Hospital and Canadian food allergy advocacy organizations. Data were collected on initial allergic reactions to peanut and treatment inside and outside HCFs. Of 751 individuals who had an allergic reaction to peanut, 613 responded (81.6%). Initial reactions were mild in 28.4% (95% CI, 25.0-32.1%), moderate in 50.6% (46.6-54.6%), and severe in 20.9% (17.8-24.3%). Average age of initial reaction was 2.1 years (2.0-2.3). Among participants, 11.6% (9.1-14.7%) were diagnosed with peanut allergy (based on skin and IgE testing) prior to the initial reaction. Of the 613 participants, 32.1% (28.5-36.0%) were treated in HCFs only, 51.7% (47.7-55.7) outside HCFs only, and 16.2% (13.4-19.3%) in both. 21.3% (17.0-26.3%) of all reactions treated in HCFs received epinephrine (table) versus only 3% (1.8-5.1%) treated outside. Of those with moderate or severe initial reactions, 58.2% (53.5-62.8%) were treated in HCFs, and 23.9% (19.1-29.6%) of these received epinephrine. See table 1 . Almost 50% of individuals with potentially life-threatening initial reactions are not treated in HCFs. Further, for those with moderate and severe reactions treated in HCFs, there is substantial underuse of epinephrine. Thus, it is crucial to establish, distribute, and monitor treatment guidelines that would improve treatment practices of food-related allergic reactions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".