Assessing the Emergent Public Health Concern of All-Terrain Vehicle Injuries in Rural and Agricultural Environments: Initial Review of Available National Datasets in the United States
Bibliographic record
Abstract
BACKGROUND: Injuries related to the operation of off-road vehicles (ORVs), including all-terrain vehicles (ATVs), continue to be a significant public health concern, especially in rural and agricultural environments. In the United States alone, ATVs have played a role in thousands of fatalities and millions of injuries in the recent decades. However, no known centralized federal surveillance system consistently captures these data. Traditional injury data sources include surveys, police reports, trauma registries, emergency department data, newspaper and online media reports, and state and federal agency databases. OBJECTIVE: The objectives of this study paper were to (1) identify published articles on ORV-related injuries and deaths that used large databases and determine the types of datasets that were used, (2) examine and describe several national US-based surveillance systems that capture ORV-related injuries and fatalities, and (3) promote and provide support for the establishment of a federally-funded agricultural injury surveillance system. METHODS: In this study, we examined several national United States-based injury datasets, including the web-based AgInjuryNews, the Fatality Analysis Reporting System, databases compiled by the US Consumer Product Safety Commission, and the National Fatality Review Case Reporting System. RESULTS: Our review found that these data sources cannot provide a complete picture of the incidents or the circumstantial details needed to effectively inform ORV injury prevention efforts. This is particularly true with regard to ORV-related injuries in agricultural production. CONCLUSIONS: We encourage the establishment of a federally funded national agricultural injury surveillance system. However, in lieu of this, use of multiple data sources will be necessary to provide a more complete picture of ORV- and other agriculture-related injuries and fatalities.
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 | 0.029 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.035 | 0.035 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".