Epidemiology of nonpowdered firearm injury in high-income countries: a scoping review
Bibliographic record
Abstract
BACKGROUND: Nonpowdered firearms discharge projectiles at forces capable of inflicting serious harm. Regulations governing access and use of non-powdered firearms vary by jurisdiction, and the injury burden resulting from these weapons is not well described and may also vary by region. Measurement and comparison of nonpowdered firearm injury rates is important to inform injury prevention strategies. We aimed to describe published rates of nonpowdered firearm injuries across Organization for Economic Co-Operation and Development (OECD) countries. METHODS: We searched MEDLINE, EMBASE, the Cochrane Library, Web of Science, Scopus, and Criminal Justice Abstracts databases from inception to April 7, 2023 for sources reporting population-level rates of nonpowdered firearm injuries (ball bearing, airsoft, or pellet guns). We excluded case reports, case series, and experimental studies. We identified grey literature through targeted webpages and Google search engine. We extracted population and injury characteristics, rates of injury, and intent and severity data. RESULTS: We identified 31 sources from five countries (United States, Canada, United Kingdom, Finland, and Sweden) that reported rates of nonpowdered firearm injury. Data were heterogenous in terms of population, year (range: 1970-2021), injury type (ocular, head/neck, all injuries). Most data sources reported high unintentional injury burden, however, a substantial number of injuries reported were due to assaults. Younger age groups (5- to 9- and 10- to 14-year-olds) were disproportionately impacted. CONCLUSIONS: Nonpowdered firearms are an important cause of injury. Gaps in reporting and lack of uniformity exist in defining these injuries and must be addressed to inform injury prevention strategies.
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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 | 0.037 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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; both teacher heads agree on what is shown here.
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".