Formative and validation human factors studies of a new disposable prefilled injection device for subcutaneous delivery of acthar gel (repository corticotropin injection)
Bibliographic record
Abstract
BACKGROUND: The administration of repository corticotropin injection (Acthar Gel) via a single-dose prefilled injector (SelfJect) is intended to provide a simple, ergonomic alternative to traditional injection. Iterative human factors (HF) studies were conducted to identify potential use deviations and ensure appropriate device use. RESEARCH DESIGN AND METHODS: This article presents seven formative studies, a validation study (with prior pilot validation studies), and a supplemental validation study with participants including lay users, patients, caregivers, and healthcare providers. Participant interactions with SelfJect and the user interface were assessed. Use deviations, user preferences, and participants' ability to successfully complete tasks were evaluated to generate modifications to the device and user interface. RESULTS: In the validation study, 91% of participants successfully administered their first injection. Use errors were rare with simulated-use (6.9%) and knowledge-based (1.6%) testing. Use deviations were commonly attributed to experimental artifact or information oversight, and device warming had the most use errors (49% of participants), even with extensive testing and adjustments to the user interface. CONCLUSIONS: SelfJect was able to be used in a safe and effective manner by the intended users. Iterative HF studies informed the mitigation of use-related risks to reduce the occurrence of use deviations during simulated use.
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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 | 0.072 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".