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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".