Road traffic injuries and fatalities among drivers distracted by mobile devices
Bibliographic record
Abstract
CONTEXT: With increasing ownership of mobile devices (i.e., cell phones and smartphones), it is important to better understand the role of these devices in motor vehicle collision (MVC)-related trauma. AIMS: The primary objective was to synthesize evidence on the proportion of drivers injured or killed in an MVC attributed to driver distraction by a mobile device. As a secondary objective, we assessed for associations between injury risk and mobile device use while driving. SETTINGS AND DESIGN: This study was a systematic review. SUBJECTS AND METHODS: We searched five electronic databases (PubMed, Embase, CINAHL, TRIS, and Web of Science) and the gray literature to identify reports of drivers injured (regardless of the severity) or killed in MVCs attributed to mobile device-related distraction by the driver. We evaluated study and driver characteristics, as well as associations between injury risk and mobile device use by drivers. STATISTICAL ANALYSIS USED: Descriptive statistics were used to report study characteristics. The proportion of injuries related to driver distraction by mobile devices was calculated for each study. RESULTS: Overall, 4907 articles were screened, of which 13 met eligibility criteria. The median proportion of distracted-driving-related trauma was 3.4% (range: 0.04% to 44.7%). Three studies evaluated the association between mobile device use and road traffic injury; all found use of a mobile device while driving significantly increased crash risk. CONCLUSIONS: The proportion of road traffic injuries and fatalities attributed to driver distraction by a mobile device ranges from 0.04% to 44.7%. Studies were subject to limitations in the collection of reliable data on distraction-related MVCs.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".