Epinephrine autoinjectors: individualizing device and dosage to optimize anaphylaxis management in the community setting
Bibliographic record
Abstract
Background: Anaphylaxis is the most severe manifestation of a systemic allergic reaction, and, in the community setting, the immediate administration of an epinephrine autoinjector (EAI) can be life-saving. Physicians are tasked with selecting the most appropriate EAI for each individual and counseling patients and/or their caregivers to maximize the likelihood of successful deployment of the EAI. Objective: To offer an evidence-based expert clinical perspective on how physicians might best tailor EAI selection to their patients with anaphylaxis. Methods: A group of eight adult and pediatric allergists with expertise in anaphylaxis management reviewed and assessed the published data and guidelines on anaphylaxis management and EAI device selection. Results: Personalized EAI selection is influenced by intrinsic individual factors, extrinsic factors such as the properties of the individual EAI (e.g., dose, needle length, overall design) as well as cost and coverage. The number and the variety of EAIs available have expanded in most jurisdictions in recent years, which provide a greater diversity of options to meet the characteristics and needs of patients with anaphylaxis. Conclusion: There currently are no EAIs with customizable dose and needle length. Although precise personalization of each patient's EAI remains an optimistic future aspiration, careful consideration of all variables when prescribing EAIs can support optimal management of anaphylaxis.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".