Open-source 3-D printable autoinjector: Design, testing, and regulatory limitations
Bibliographic record
Abstract
Autoinjectors have become popular modern injectable medical devices used as drug delivery systems. Due to their ease, capability and reliability compared to other conventional injectable medical devices, the market and manufacturing demand for autoinjector devices are increasing rapidly and expected to reach a market of $37.5 billion globally by 2025. Although autoinjectors can offset healthcare treatment costs through self-administered medication, they can be expensive for consumers, which limit their accessibility. This study describes the design and manufacture of a spring-driven and 3-D printed autoinjector to overcome this economic accessibility challenge. The digitally replicable device is released as open-source hardware to enable low-cost distributed manufacturing. The bill of materials and assembly instructions are detailed, and the effectiveness of the autoinjector is tested against the current standard (ISO 11608-1:2022) for needle-based injection systems. The safety and dosing accuracy was tested by measuring the weight of 100% ethyl alcohol expelled from six BD Insulin syringes with varying capacities or needle lengths. A one-way analysis assessed the variability between the dose delivery efficiency of 1mL, 0.5mL, and 0.3mL syringes. Testing indicated that the entire dose was delivered over 97.5% of the time for 1mL and 0.5mL syringes, but the autoinjector's loaded spring force and size exceeded structural limitations of 0.3mL or smaller syringes. Components can be manufactured in about twelve hours using an open-source desktop RepRap-class fused filament 3-D printer. The construction requires two compression springs and 3-D printed parts. The total material cost of CAD$6.83 is less than a tenth of comparable commercial autoinjectors, which makes this approach promising. The autoinjector, however, is a class two medical device and must be approved by regulators. Future work is needed to make distributed manufacturing of such medical devices feasible and reliable to support individuals burdened by healthcare costs.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".