Current trends in needle-free jet injection: an update
Bibliographic record
Abstract
BACKGROUND: Jet injection can be defined as a needle-free drug delivery method in which a high-speed stream of fluid impacts the skin and delivers a drug. Despite 75 years of existence, it never reached its full potential as a strategic tool to deliver medications through the skin. OBJECTIVE: The aim of this review was to evaluate and summarize the evolution of jet injection intradermal drug delivery method including technological advancements and new indications for use. METHODS: A review of the literature was performed with no limits placed on publication date. RESULTS: Needleless injectors not only reduce pain during drug delivery but also confine the drug more evenly in the dermis. Understanding skin properties of the injection site is a key factor to obtain optimal results as well as setting the right parameters of the jet injector. Until the advent of disposable jet injectors/cartridges, autoclaving of the injector remains the only reliable method to eliminate the risk of infection. Needle-free intradermal injection using corticosteroids and/or local anesthetics is well documented with promising indications being developed. LIMITATIONS: Limitations of the review include low-quality evidence, small sample sizes, varying treatment parameters, and publication bias. CONCLUSION: New developments may help reconsider the use of jet injection technology. Future studies should focus on measurable optimized parameters to insure a safe and effective outcome.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".