Hooked epinephrine auto-injector devices in children: four case reports with three different proposed mechanisms
Bibliographic record
Abstract
BACKGROUND: The prevalence of epinephrine auto-injectors (EAI) use is on the rise. Our objective was to describes children with hooked EAI needles that were embedded in soft tissues. CASE PRESENTATION: Results: Two children self-injected in their shins. The embedded EAIs required removal in the Emergency Department. Both needles were hooked and splayed at the tip. A boy in anaphylaxis kicked his leg during EAI injection and the hooked needle embedded under his skin and was difficult to dislodge. The exposed needle was curved. A girl had an EAI administered for anaphylaxis, which was also difficult to dislodge. On removal, the distal needle tip was hooked approximately 160 degrees. Images of the device revealed that the needle fired off-center from the device and the device components were cracked. We propose three different explanations for these hooked EAI needles. The first is that the needle could hit bone during injection and curve rather than penetrates further. Secondly, the needle could bend when the patient moves during injection. Thirdly, if a needle fires sufficiently off-center to hit the cartridge carrier, this could hook the needle prior to injection. CONCLUSIONS: Awareness of the reasons for needle hooking, damage observed, and challenges and successful approaches to their removal, can better prepare the provider for these uncommon events. Teaching parents, children and educators about safe EAI storage and appropriate restraint during use may prevent some of these accidental injuries. Reporting device failures may lead to improvements in device performance and design.
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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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".