The Reliability of Auto-Injectors in Clinical Use: A Systematic Review
Bibliographic record
Abstract
Auto-injectors are medical devices designed for the self-administration of injections by patients and for easy administration by healthcare professionals in emergency situations. Although they vary in design and application, auto-injectors are typically built around a spring-loaded syringe. Despite their widespread use in a variety of clinical settings, there have been limited attempts to assess their reliability. This systematic review investigates the reliability of auto-injectors, identifies common causes of failure, and summarizes the overall rate of malfunction. A systematic review of research published on the PubMed and Cochrane Library databases was performed in July 2022. The relevant studies were assessed for their methodological quality and risk of bias prior to extracting key study outcomes on auto-injector reliability. Finally, a summary rate covering all eligible studies was calculated. The search identified a total of 110 articles, of which ten were found to be suitable for inclusion. The risk of bias was low, and the methodological quality was high across the ten studies. Out of a total of 2,964 injections administered from an auto-injector, there were 12 device malfunctions, giving a summary rate of 0.40% (±0.23) auto-injector failures. The causes of malfunction varied in nature, with the majority of cases (58.3%) not being specified or not identified. This review has demonstrated that auto-injectors are reliable devices. Although further research on the nature of malfunctions is needed, the low rate of malfunctions supports training programs for healthcare professionals and patients on the optimum use and maintenance of auto-injectors. It provides a rationale for their continued development.
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 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 | 0.027 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".