Do epinephrine auto-injectors have an unsuitable needle length in children and adolescents at risk for anaphylaxis from food allergy?
Bibliographic record
Abstract
BACKGROUND: Food allergy is the most common cause of anaphylaxis in children. Intramuscular delivery of epinephrine auto-injectors (EAI) is the standard of care for the treatment of anaphylaxis. We examined if children and adolescents at risk of anaphylaxis weighing 15-30 kg and >30 kg would receive epinephrine into the intramuscular space with the currently available EAI in North America and Europe. METHODS: The distance from skin to muscle (STMD) and skin to bone (STBD) on the mid third anterolateral area of the right thigh was measured by ultrasound applying either high pressure (max) or slight pressure (min) in 102 children weighing 15-30 kg (group 1) and 100 children and adolescents, weighing more than 30 kg (group 2). RESULTS: Using a high pressure EAI (HPEAI), Epipen Jr(®) and Auvi-Q(®)/Allerject(®) 0.15 mg, 11/102 (11 %) children in group 1 and 38/102 (38 %) using another HPEAI, Jext(®), had a STMDmax that showed a risk of intraosseous injection. There was a 1 % risk of subcutaneous injection with these devices. There was no risk of intraosseous injection using a low pressure EAI (LPEAI), Emerade(®). In group 2, the risk of intraosseous injection using a HPEAI was 3 % and no risk using a LPEAI. However, the risk of subcutaneous injection using HPEAI was 9 % and using LPEAI was 2 %. CONCLUSION: There is a risk of intraosseous injection using HPEAI (Epipen(®)/Epipen Jr(®), Auvi-Q(®)/Allerject(®) and especially Jext(®)) in children at risk of anaphylaxis. There was also a risk of subcutaneous injection using the currently available HPEAI in children and adolescents.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".