Bibliographic record
Abstract
Abstract Background Epinephrine auto-injectors are expected to deliver the drug intramuscularly. Objective To study whether injection through clothing influences the frequency of subcutaneous and intraosseous/periosteal deposition of epinephrine. Methods Skin to muscle and skin to bone distances were measured for 303 children and adolescents and 99 adults. Distance was determined by ultrasound, with high or low pressure on the ultrasound probe. The risk/percentage of subcutaneous and intraosseous/periosteal injections was calculated using the lower and upper limits for the authority-approved length of EAI needles as provided by two high pressure EAI manufacturers and one low pressure EAI manufacturer. The addition winter clothing on the delivery of epinephrine was illustrated by comparing drug delivery fissue depth with no clothes. Furthermore, the riof non-intramuscular delivery for the shortest and longest approved needle length was calculated. Results When using EpipenJr® in children < 15 kg the risk of intraosseous/periostal injection was reduced from 1% and 59% for the shortest and longest approved needle length to 0 and 15% with winter clothes. The Auvi-Q® 0.1 mg had no risk of intraosseous/periosteal injection. However, the subcutaneous deposition risk increased from 94% and 28% to 100% and 99% with winter clothes. The risk of subcutaneous injection using EpipenJr® in the youngest children increased from 13% and 0% to 81% and 1% with winter clothes, and with Epipen® in adults from 45% and 17% to 60% and 38%. Emerade®, had a risk of subcutaneous injection in adults increasing from 14% and 10% to 28% and 21% adding winter clothes. Conclusion The risk of intraosseous/periosteal injections decreases and the risk of subcutaneous injection increases when injecting through winter clothes for all EAIs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".