10 practical priorities to prevent and manage serious allergic reactions: GA<sup>2</sup>LEN ANACare and EFA Anaphylaxis Manifesto
Bibliographic record
Abstract
This Anaphylaxis Manifesto calls on communities to prioritise 10 practical actions to improve the lives of people at risk of serious allergic reactions. The Global Allergy and Asthma European Network and the European Federation of Allergy and Airways Diseases Patients' Associations (EFA) compiled patient-centric priorities. We used qualitative consensus methods, research evidence and feedback from over 200 patient groups, stakeholder organisations and healthcare professionals. We encourage healthcare, education and food organisations to collaborate with people at risk of serious allergic reactions to tackle safety, anxiety and financial burdens for individuals and societies. Key priorities for prevention include awareness-raising campaigns for the public and professionals, school and workplace initiatives and mandatory precautionary allergen labels on food. Priorities for improving immediate and long-term management include educating healthcare professionals, patients and schools about when and how to use adrenaline, funding two approved adrenaline devices for everyone at risk, and facilitating access to allergy specialists. Integrated care pathways should include clinical and non-clinical management options such as individualised risk assessment and quality of life assessment, self-management plans, dietetic and psychosocial support and peer support. Organisations around the world are committing to work together towards these priorities.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".