Anaphylaxis, killer allergy : long-term management in the community
Bibliographic record
Abstract
Traditionally, physicians are trained to diagnose and treat anaphylaxis as an acute emergency in a health care setting. In addition to this crucial and time-honored role, we should be cognizant of our wider responsibility to (1) provide a risk assessment for individuals with anaphylaxis, (2) prevent future anaphylaxis episodes by developing long-term personalized risk reduction strategies for affected individuals, and (3) emphasize anaphylaxis education. Risk assessment should include verification of the trigger factor or factors for the anaphylaxis episode by obtaining a comprehensive history and performing relevant investigations, including allergen skin tests and measurement of allergen-specific IgE in serum. In addition, the potential effect of comorbidities and concurrently administered medications on the recognition and emergency treatment of subsequent episodes should be determined. Risk reduction strategies should be personalized to include information about avoidance of specific triggers and initiation of relevant specific preventive treatment (eg, venom immunotherapy). At-risk individuals should be coached in the use of self-injectable epinephrine and equipped with an anaphylaxis emergency action plan and with accurate medical identification. Anaphylaxis education should be provided for these individuals, their families and caregivers, health care professionals, and the general public. Further development of an optimal diagnostic test for anaphylaxis and of tests and algorithms to predict future risk and prevent fatality are urgently needed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".