Factors pertaining to road traffic injuries; a systematic scoping review and meta-analysis
Bibliographic record
Abstract
Abstract Background Many factors have been associated with road traffic injuries (RTI) while no study has cumulatively gathered and pooled them. Methods A comprehensive search was carried out in PubMed, World of Sciences, and Scopus based on predefined keywords. Two independent reviewers performed screening the search findings and data extraction procedure. Risk of bias was checked based on the Newcastle–Ottawa Scale. Odds ratios (OR) were extracted and pooled by meta-analysis to reach the overall effect. Results In all, 34 studies were included that summed 277,943 individuals, reporting at least one factor pertaining to RTI. The factors related to an increased rate of RTIs (OR 1.49, 95% confidence interval 1.35–1.64). Based on the included publications, factors could be grouped as alcohol consumption (1.74, 1.32–2.30), experienced driving and self-confidence (106, 0.94–1.20), seatbelt fastening incompliance (1.45, 1.39–1.51), driving in poor roads and rural areas (1.31, 1.03–1.42), lower education level (1.30, 1.17–1.44), lower income (1.23, 1.13–1.33), malfunctioned vehicle (1.77, 1.52–2.05), and driving with psychological tensions (1.20, 1.11–1.30). Conclusion Almost all the discovered factors in this study were among the modifiable factors that highlight the need for preventive and debarment measures.
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 | 0.030 | 0.089 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.022 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| 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".