Drug-device combination products in the twenty-first century: epinephrine auto-injector development using human factors engineering
Bibliographic record
Abstract
INTRODUCTION: The systematic application of human factors engineering (HFE) principles to the development of drug-device combination products, including epinephrine auto-injectors (EAIs), has the potential to improve the effectiveness and safety of drug administration. AREAS COVERED: A PubMed search was performed to assess the role of HFE in the development of drug-device combination products. The following keywords were used in different combinations: 'human factors engineering,' 'human factors,' 'medical products,' 'epinephrine/adrenaline auto-injector,' 'healthcare' and 'patient safety.' This review provides a summary of HFE principles and their application to the development of drug-device combination products as advised by the US FDA. It also describes the HFE process that was applied to the development of Auvi-Q, a novel EAI, highlighting specific steps that occurred during the product-development program. EXPERT OPINION: For drug-device combination products, device labeling and usability are critical and have the potential to impact clinical outcomes. Application of HFE principles to the development of drug-delivery devices has the potential to improve product quality and reliability, reduce risk and improve patient safety when applied early in the development process. Additional clinical and real-world studies will confirm whether the application of HFE has helped to develop an EAI that better meets the needs of patients at risk of anaphylaxis.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".